{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/object-tracking/papers/7","list_of":"/task/object-tracking","task":"Object Tracking","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":7,"pages_in_order":20,"rows_per_page":100,"rows":[601,700],"of":1966,"counts":{"archive_papers_tagged":1966,"with_a_code_link":767,"where_syntology_ran_a_sample":138,"not_listed_spam_title":0,"listed":1966,"listed_where_code_ran":138,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":122,"every_run_a_failure_of_syntologys_instrument":16,"listed_with_a_run_with_no_instrument_failure":122,"listed_every_run_a_failure_of_syntologys_instrument":16,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/object-tracking","prev":"/task/object-tracking/papers/6","next":"/task/object-tracking/papers/8","papers":[{"url":"/paper/multiple-convolutional-features-in-siamese","slug":"multiple-convolutional-features-in-siamese","title":"Multiple Convolutional Features in Siamese Networks for Object Tracking","date":"2021-03-01","arxiv_id":"2103.01222","repositories_listed":1,"syntology":null},{"url":"/paper/4d-panoptic-lidar-segmentation","slug":"4d-panoptic-lidar-segmentation","title":"4D Panoptic LiDAR Segmentation","date":"2021-02-24","arxiv_id":"2102.12472","repositories_listed":1,"syntology":null},{"url":"/paper/the-multi-temporal-urban-development-spacenet","slug":"the-multi-temporal-urban-development-spacenet","title":"The Multi-Temporal Urban Development SpaceNet Dataset","date":"2021-02-08","arxiv_id":"2102.04420","repositories_listed":1,"syntology":null},{"url":"/paper/deft-detection-embeddings-for-tracking","slug":"deft-detection-embeddings-for-tracking","title":"DEFT: Detection Embeddings for Tracking","date":"2021-02-03","arxiv_id":"2102.02267","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-appearance-modeling-with-multi","slug":"discriminative-appearance-modeling-with-multi","title":"Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking","date":"2021-01-28","arxiv_id":"2101.12159","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-the-details-when-evaluating-a","slug":"revisiting-the-details-when-evaluating-a","title":"Revisiting the details when evaluating a visual tracker","date":"2021-01-25","arxiv_id":"2102.06733","repositories_listed":1,"syntology":null},{"url":"/paper/all-day-object-tracking-for-unmanned-aerial","slug":"all-day-object-tracking-for-unmanned-aerial","title":"All-Day Object Tracking for Unmanned Aerial Vehicle","date":"2021-01-21","arxiv_id":"2101.08446","repositories_listed":1,"syntology":null},{"url":"/paper/object-tracking-by-detection-with-visual-and","slug":"object-tracking-by-detection-with-visual-and","title":"Object Tracking by Detection with Visual and Motion Cues","date":"2021-01-19","arxiv_id":"2101.07549","repositories_listed":1,"syntology":null},{"url":"/paper/semi-automatic-video-annotation-for-object","slug":"semi-automatic-video-annotation-for-object","title":"Semi-Automatic Annotation For Visual Object Tracking","date":"2021-01-18","arxiv_id":"2101.06977","repositories_listed":1,"syntology":null},{"url":"/paper/cityflow-nl-tracking-and-retrieval-of","slug":"cityflow-nl-tracking-and-retrieval-of","title":"CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions","date":"2021-01-12","arxiv_id":"2101.04741","repositories_listed":1,"syntology":null},{"url":"/paper/temporally-guided-articulated-hand-pose","slug":"temporally-guided-articulated-hand-pose","title":"Temporally Guided Articulated Hand Pose Tracking in Surgical Videos","date":"2021-01-12","arxiv_id":"2101.04281","repositories_listed":1,"syntology":null},{"url":"/paper/horizontal-to-vertical-video-conversion","slug":"horizontal-to-vertical-video-conversion","title":"Horizontal-to-Vertical Video Conversion","date":"2021-01-11","arxiv_id":"2101.04051","repositories_listed":1,"syntology":null},{"url":"/paper/assignment-space-based-multi-object-tracking","slug":"assignment-space-based-multi-object-tracking","title":"Assignment-Space-Based Multi-Object Tracking and Segmentation","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/continuous-copy-paste-for-one-stage-multi","slug":"continuous-copy-paste-for-one-stage-multi","title":"Continuous Copy-Paste for One-Stage Multi-Object Tracking and Segmentation","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/depthtrack-unveiling-the-power-of-rgbd-1","slug":"depthtrack-unveiling-the-power-of-rgbd-1","title":"DepthTrack: Unveiling the Power of RGBD Tracking","date":"2021-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dvd-a-diagnostic-dataset-for-multi-step","slug":"dvd-a-diagnostic-dataset-for-multi-step","title":"DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue","date":"2021-01-01","arxiv_id":"2101.00151","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-3d-multi-modal-multi-object","slug":"probabilistic-3d-multi-modal-multi-object","title":"Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving","date":"2020-12-26","arxiv_id":"2012.13755","repositories_listed":1,"syntology":null},{"url":"/paper/coarse-to-fine-object-tracking-using-deep","slug":"coarse-to-fine-object-tracking-using-deep","title":"Coarse-to-Fine Object Tracking Using Deep Features and Correlation Filters","date":"2020-12-23","arxiv_id":"2012.12784","repositories_listed":1,"syntology":null},{"url":"/paper/objectron-a-large-scale-dataset-of-object","slug":"objectron-a-large-scale-dataset-of-object","title":"Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations","date":"2020-12-18","arxiv_id":"2012.09988","repositories_listed":1,"syntology":null},{"url":"/paper/alpha-refine-boosting-tracking-performance-by-1","slug":"alpha-refine-boosting-tracking-performance-by-1","title":"Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation","date":"2020-12-12","arxiv_id":"2012.06815","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-transformation-for-self","slug":"contrastive-transformation-for-self","title":"Contrastive Transformation for Self-supervised Correspondence Learning","date":"2020-12-09","arxiv_id":"2012.05057","repositories_listed":1,"syntology":null},{"url":"/paper/sftrack-a-fast-learnable-spectral","slug":"sftrack-a-fast-learnable-spectral","title":"SFTrack++: A Fast Learnable Spectral Segmentation Approach for Space-Time Consistent Tracking","date":"2020-11-27","arxiv_id":"2011.13843","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/sftrack-a-fast-learnable-spectral#ran","syntology_url":"https://syntology.ai/paper/2011.13843","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13843"}},"official":{"repos":["bit-ml/sftrackpp"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/an-occlusion-aware-edge-based-method-for","slug":"an-occlusion-aware-edge-based-method-for","title":"An Occlusion‐aware Edge‐Based Method for Monocular 3D Object Tracking using Edge Confidence.","date":"2020-11-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gmot-40-a-benchmark-for-generic-multiple","slug":"gmot-40-a-benchmark-for-generic-multiple","title":"GMOT-40: A Benchmark for Generic Multiple Object Tracking","date":"2020-11-24","arxiv_id":"2011.11858","repositories_listed":1,"syntology":null},{"url":"/paper/siamese-tracking-with-lingual-object","slug":"siamese-tracking-with-lingual-object","title":"Siamese Tracking with Lingual Object Constraints","date":"2020-11-23","arxiv_id":"2011.11721","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-voxel-based-3d-object","slug":"uncertainty-aware-voxel-based-3d-object","title":"Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss","date":"2020-11-04","arxiv_id":"2011.02553","repositories_listed":1,"syntology":null},{"url":"/paper/smot-single-shot-multi-object-tracking","slug":"smot-single-shot-multi-object-tracking","title":"SMOT: Single-Shot Multi Object Tracking","date":"2020-10-30","arxiv_id":"2010.16031","repositories_listed":1,"syntology":null},{"url":"/paper/approxdet-content-and-contention-aware","slug":"approxdet-content-and-contention-aware","title":"ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles","date":"2020-10-21","arxiv_id":"2010.10754","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-analysis-of-visual-features-for","slug":"an-empirical-analysis-of-visual-features-for","title":"An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes","date":"2020-10-15","arxiv_id":"2010.07881","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-segmentation-tracking-using","slug":"discriminative-segmentation-tracking-using","title":"Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking","date":"2020-09-21","arxiv_id":"2009.09669","repositories_listed":1,"syntology":null},{"url":"/paper/daer-to-reject-seeds-with-dual-loss","slug":"daer-to-reject-seeds-with-dual-loss","title":"Ground-truth or DAER: Selective Re-query of Secondary Information","date":"2020-09-16","arxiv_id":"2009.07414","repositories_listed":1,"syntology":null},{"url":"/paper/lasot-a-high-quality-large-scale-single","slug":"lasot-a-high-quality-large-scale-single","title":"LaSOT: A High-quality Large-scale Single Object Tracking Benchmark","date":"2020-09-08","arxiv_id":"2009.03465","repositories_listed":1,"syntology":null},{"url":"/paper/e-tld-event-based-framework-for-dynamic","slug":"e-tld-event-based-framework-for-dynamic","title":"e-TLD: Event-based Framework for Dynamic Object Tracking","date":"2020-09-02","arxiv_id":"2009.00855","repositories_listed":1,"syntology":null},{"url":"/paper/deep-probabilistic-feature-metric-tracking","slug":"deep-probabilistic-feature-metric-tracking","title":"Deep Probabilistic Feature-metric Tracking","date":"2020-08-31","arxiv_id":"2008.13504","repositories_listed":1,"syntology":null},{"url":"/paper/online-multi-object-tracking-and-segmentation","slug":"online-multi-object-tracking-and-segmentation","title":"Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion","date":"2020-08-31","arxiv_id":"2009.00100","repositories_listed":1,"syntology":null},{"url":"/paper/robust-long-term-object-tracking-via-improved","slug":"robust-long-term-object-tracking-via-improved","title":"Robust Long-Term Object Tracking via Improved Discriminative Model Prediction","date":"2020-08-11","arxiv_id":"2008.04722","repositories_listed":1,"syntology":null},{"url":"/paper/appearance-free-tripartite-matching-for","slug":"appearance-free-tripartite-matching-for","title":"Appearance-free Tripartite Matching for Multiple Object Tracking","date":"2020-08-09","arxiv_id":"2008.03628","repositories_listed":1,"syntology":null},{"url":"/paper/how-trustworthy-are-the-existing-performance","slug":"how-trustworthy-are-the-existing-performance","title":"How Trustworthy are Performance Evaluations for Basic Vision Tasks?","date":"2020-08-08","arxiv_id":"2008.03533","repositories_listed":1,"syntology":null},{"url":"/paper/towards-accurate-pixel-wise-object-tracking","slug":"towards-accurate-pixel-wise-object-tracking","title":"Towards Accurate Pixel-wise Object Tracking by Attention Retrieval","date":"2020-08-06","arxiv_id":"2008.02745","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-accurate-pixel-wise-object-tracking#ran","syntology_url":"https://syntology.ai/paper/2008.02745","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.02745"}},"official":{"repos":["researchmm/TracKit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lsotb-tir-a-large-scale-high-diversity","slug":"lsotb-tir-a-large-scale-high-diversity","title":"LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark","date":"2020-08-03","arxiv_id":"2008.00836","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-object-tracking-with-cycle","slug":"self-supervised-object-tracking-with-cycle","title":"Self-supervised Object Tracking with Cycle-consistent Siamese Networks","date":"2020-08-03","arxiv_id":"2008.00637","repositories_listed":1,"syntology":null},{"url":"/paper/multi-target-tracking-with-an-adaptive-d-glmb","slug":"multi-target-tracking-with-an-adaptive-d-glmb","title":"Multi-object Tracking with an Adaptive Generalized Labeled Multi-Bernoulli Filter","date":"2020-08-02","arxiv_id":"2008.00413","repositories_listed":1,"syntology":null},{"url":"/paper/towards-robust-visual-tracking-for-unmanned","slug":"towards-robust-visual-tracking-for-unmanned","title":"Towards Robust Visual Tracking for Unmanned Aerial Vehicle with Tri-Attentional Correlation Filters","date":"2020-08-02","arxiv_id":"2008.00528","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-object-tracking-and-masking-for","slug":"dynamic-object-tracking-and-masking-for","title":"Dynamic Object Tracking and Masking for Visual SLAM","date":"2020-07-31","arxiv_id":"2008.00072","repositories_listed":1,"syntology":null},{"url":"/paper/chained-tracker-chaining-paired-attentive","slug":"chained-tracker-chaining-paired-attentive","title":"Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking","date":"2020-07-29","arxiv_id":"2007.14557","repositories_listed":1,"syntology":null},{"url":"/paper/a-hybrid-neuromorphic-object-tracking-and","slug":"a-hybrid-neuromorphic-object-tracking-and","title":"A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systems","date":"2020-07-21","arxiv_id":"2007.11404","repositories_listed":1,"syntology":null},{"url":"/paper/scale-equivariance-improves-siamese-tracking","slug":"scale-equivariance-improves-siamese-tracking","title":"Scale Equivariance Improves Siamese Tracking","date":"2020-07-17","arxiv_id":"2007.09115","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/scale-equivariance-improves-siamese-tracking#ran","syntology_url":"https://syntology.ai/paper/2007.09115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09115"}},"official":{"repos":["isosnovik/SiamSE"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-heterogeneous-autoencoder-for-subspace","slug":"deep-heterogeneous-autoencoder-for-subspace","title":"Unsupervised Spatio-temporal Latent Feature Clustering for Multiple-object Tracking and Segmentation","date":"2020-07-14","arxiv_id":"2007.07175","repositories_listed":1,"syntology":null},{"url":"/paper/a-distilled-model-for-tracking-and-tracker","slug":"a-distilled-model-for-tracking-and-tracker","title":"Tracking-by-Trackers with a Distilled and Reinforced Model","date":"2020-07-08","arxiv_id":"2007.04108","repositories_listed":1,"syntology":null},{"url":"/paper/pointtrack-for-effective-online-multi-object","slug":"pointtrack-for-effective-online-multi-object","title":"PointTrack++ for Effective Online Multi-Object Tracking and Segmentation","date":"2020-07-03","arxiv_id":"2007.01549","repositories_listed":1,"syntology":null},{"url":"/paper/segment-as-points-for-efficient-online-multi","slug":"segment-as-points-for-efficient-online-multi","title":"Segment as Points for Efficient Online Multi-Object Tracking and Segmentation","date":"2020-07-03","arxiv_id":"2007.01550","repositories_listed":1,"syntology":null},{"url":"/paper/lifted-disjoint-paths-with-application-in-1","slug":"lifted-disjoint-paths-with-application-in-1","title":"Lifted Disjoint Paths with Application in Multiple Object Tracking","date":"2020-06-25","arxiv_id":"2006.14550","repositories_listed":1,"syntology":null},{"url":"/paper/joint-detection-and-multi-object-tracking","slug":"joint-detection-and-multi-object-tracking","title":"Joint Object Detection and Multi-Object Tracking with Graph Neural Networks","date":"2020-06-23","arxiv_id":"2006.13164","repositories_listed":1,"syntology":null},{"url":"/paper/gnn3dmot-graph-neural-network-for-3d-multi-1","slug":"gnn3dmot-graph-neural-network-for-3d-multi-1","title":"GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning","date":"2020-06-12","arxiv_id":"2006.07327","repositories_listed":1,"syntology":null},{"url":"/paper/unmasking-the-inductive-biases-of","slug":"unmasking-the-inductive-biases-of","title":"Benchmarking Unsupervised Object Representations for Video Sequences","date":"2020-06-12","arxiv_id":"2006.07034","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unmasking-the-inductive-biases-of#ran","syntology_url":"https://syntology.ai/paper/2006.07034","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07034"}},"official":{"repos":["ecker-lab/object-centric-representation-benchmark"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/map3d-registration-based-multi-object","slug":"map3d-registration-based-multi-object","title":"Map3D: Registration Based Multi-Object Tracking on 3D Serial Whole Slide Images","date":"2020-06-10","arxiv_id":"2006.06038","repositories_listed":1,"syntology":null},{"url":"/paper/tubetk-adopting-tubes-to-track-multi-object-1","slug":"tubetk-adopting-tubes-to-track-multi-object-1","title":"TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model","date":"2020-06-10","arxiv_id":"2006.05683","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/tubetk-adopting-tubes-to-track-multi-object-1#ran","syntology_url":"https://syntology.ai/paper/2006.05683","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05683"}},"official":{"repos":["BoPang1996/TubeTK"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rgb-d-e-event-camera-calibration-for-fast-6","slug":"rgb-d-e-event-camera-calibration-for-fast-6","title":"RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking","date":"2020-06-09","arxiv_id":"2006.05011","repositories_listed":1,"syntology":null},{"url":"/paper/siamese-keypoint-prediction-network-for","slug":"siamese-keypoint-prediction-network-for","title":"Siamese Keypoint Prediction Network for Visual Object Tracking","date":"2020-06-07","arxiv_id":"2006.04078","repositories_listed":1,"syntology":null},{"url":"/paper/gnn3dmot-graph-neural-network-for-3d-multi","slug":"gnn3dmot-graph-neural-network-for-3d-multi","title":"GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-neural-solver-for-multiple-object-1","slug":"learning-a-neural-solver-for-multiple-object-1","title":"Learning a Neural Solver for Multiple Object Tracking","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-irreducible-representations-of","slug":"learning-irreducible-representations-of","title":"Computing Representations for Lie Algebraic Networks","date":"2020-06-01","arxiv_id":"2006.00724","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-irreducible-representations-of#ran","syntology_url":"https://syntology.ai/paper/2006.00724","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.00724"}},"official":{"repos":["noajshu/learning_irreps"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tsdm-tracking-by-siamrpn-with-a-depth-refiner","slug":"tsdm-tracking-by-siamrpn-with-a-depth-refiner","title":"TSDM: Tracking by SiamRPN++ with a Depth-refiner and a Mask-generator","date":"2020-05-08","arxiv_id":"2005.04063","repositories_listed":1,"syntology":null},{"url":"/paper/drotrack-high-speed-drone-based-object","slug":"drotrack-high-speed-drone-based-object","title":"DroTrack: High-speed Drone-based Object Tracking Under Uncertainty","date":"2020-05-02","arxiv_id":"2005.00828","repositories_listed":1,"syntology":null},{"url":"/paper/how-to-track-your-dragon-a-multi-attentional","slug":"how-to-track-your-dragon-a-multi-attentional","title":"How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking","date":"2020-04-21","arxiv_id":"2004.10335","repositories_listed":1,"syntology":null},{"url":"/paper/fast-template-matching-and-update-for-video","slug":"fast-template-matching-and-update-for-video","title":"Fast Template Matching and Update for Video Object Tracking and Segmentation","date":"2020-04-16","arxiv_id":"2004.07538","repositories_listed":1,"syntology":null},{"url":"/paper/deformable-siamese-attention-networks-for","slug":"deformable-siamese-attention-networks-for","title":"Deformable Siamese Attention Networks for Visual Object Tracking","date":"2020-04-14","arxiv_id":"2004.06711","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-learning-improves-static-object","slug":"end-to-end-learning-improves-static-object","title":"End-to-end Learning Improves Static Object Geo-localization in Monocular Video","date":"2020-04-10","arxiv_id":"2004.05232","repositories_listed":1,"syntology":null},{"url":"/paper/retinatrack-online-single-stage-joint","slug":"retinatrack-online-single-stage-joint","title":"RetinaTrack: Online Single Stage Joint Detection and Tracking","date":"2020-03-30","arxiv_id":"2003.13870","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-and-unsupervised-detections-for","slug":"supervised-and-unsupervised-detections-for","title":"Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study","date":"2020-03-30","arxiv_id":"2003.13644","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-object-motion-and-affinity-model","slug":"a-unified-object-motion-and-affinity-model","title":"A Unified Object Motion and Affinity Model for Online Multi-Object Tracking","date":"2020-03-25","arxiv_id":"2003.11291","repositories_listed":1,"syntology":null},{"url":"/paper/know-your-surroundings-exploiting-scene","slug":"know-your-surroundings-exploiting-scene","title":"Know Your Surroundings: Exploiting Scene Information for Object Tracking","date":"2020-03-24","arxiv_id":"2003.11014","repositories_listed":1,"syntology":null},{"url":"/paper/mot20-a-benchmark-for-multi-object-tracking","slug":"mot20-a-benchmark-for-multi-object-tracking","title":"MOT20: A benchmark for multi object tracking in crowded scenes","date":"2020-03-19","arxiv_id":"2003.09003","repositories_listed":1,"syntology":null},{"url":"/paper/distnet-deep-tracking-by-displacement","slug":"distnet-deep-tracking-by-displacement","title":"DistNet: Deep Tracking by displacement regression: application to bacteria growing in the Mother Machine","date":"2020-03-17","arxiv_id":"2003.07790","repositories_listed":1,"syntology":null},{"url":"/paper/multi-drone-based-single-object-tracking-with","slug":"multi-drone-based-single-object-tracking-with","title":"Multi-Drone based Single Object Tracking with Agent Sharing Network","date":"2020-03-16","arxiv_id":"2003.06994","repositories_listed":1,"syntology":null},{"url":"/paper/keyfilter-aware-real-time-uav-object-tracking","slug":"keyfilter-aware-real-time-uav-object-tracking","title":"Keyfilter-Aware Real-Time UAV Object Tracking","date":"2020-03-11","arxiv_id":"2003.05218","repositories_listed":1,"syntology":null},{"url":"/paper/training-set-distillation-for-real-time-uav","slug":"training-set-distillation-for-real-time-uav","title":"Training-Set Distillation for Real-Time UAV Object Tracking","date":"2020-03-11","arxiv_id":"2003.05326","repositories_listed":1,"syntology":null},{"url":"/paper/argus-efficient-activity-detection-system-for","slug":"argus-efficient-activity-detection-system-for","title":"Argus: Efficient Activity Detection System for Extended Video Analysis","date":"2020-03-02","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/jrmot-a-real-time-3d-multi-object-tracker-and","slug":"jrmot-a-real-time-3d-multi-object-tracker-and","title":"JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset","date":"2020-02-19","arxiv_id":"2002.08397","repositories_listed":1,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/jrmot-a-real-time-3d-multi-object-tracker-and#ran","syntology_url":"https://syntology.ai/paper/2002.08397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.08397"}},"official":{"repos":["StanfordVL/JRMOT_ROS"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/input-dropout-for-spatially-aligned","slug":"input-dropout-for-spatially-aligned","title":"Input Dropout for Spatially Aligned Modalities","date":"2020-02-07","arxiv_id":"2002.02852","repositories_listed":1,"syntology":null},{"url":"/paper/bsuv-net-a-fully-convolutional-neural-network","slug":"bsuv-net-a-fully-convolutional-neural-network","title":"BSUV-Net: A Fully-Convolutional Neural Network forBackground Subtraction of Unseen Videos","date":"2020-01-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fooling-detection-alone-is-not-enough","slug":"fooling-detection-alone-is-not-enough","title":"Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/kernel-learning-for-visual-perception","slug":"kernel-learning-for-visual-perception","title":"Kernel learning for visual perception","date":"2019-12-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robust-visual-object-tracking-with-natural","slug":"robust-visual-object-tracking-with-natural","title":"Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers","date":"2019-12-04","arxiv_id":"1912.02048","repositories_listed":1,"syntology":null},{"url":"/paper/spstracker-sub-peak-suppression-of-response","slug":"spstracker-sub-peak-suppression-of-response","title":"SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking","date":"2019-12-02","arxiv_id":"1912.00597","repositories_listed":1,"syntology":null},{"url":"/paper/siam-r-cnn-visual-tracking-by-re-detection","slug":"siam-r-cnn-visual-tracking-by-re-detection","title":"Siam R-CNN: Visual Tracking by Re-Detection","date":"2019-11-28","arxiv_id":"1911.12836","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/siam-r-cnn-visual-tracking-by-re-detection#ran","syntology_url":"https://syntology.ai/paper/1911.12836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.12836"}},"official":null}},{"url":"/paper/d3s-a-discriminative-single-shot-segmentation","slug":"d3s-a-discriminative-single-shot-segmentation","title":"D3S -- A Discriminative Single Shot Segmentation Tracker","date":"2019-11-20","arxiv_id":"1911.08862","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-spatial-invariance-for-scalable","slug":"exploiting-spatial-invariance-for-scalable","title":"Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking","date":"2019-11-20","arxiv_id":"1911.09033","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploiting-spatial-invariance-for-scalable#ran","syntology_url":"https://syntology.ai/paper/1911.09033","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.09033"}},"official":{"repos":["e2crawfo/silot"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-global-multi-object-tracking-under","slug":"efficient-global-multi-object-tracking-under","title":"Efficient Global Multi-object Tracking Under Minimum-cost Circulation Framework","date":"2019-11-02","arxiv_id":"1911.00796","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-aware-online-adversarial","slug":"spatial-aware-online-adversarial","title":"SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking","date":"2019-10-19","arxiv_id":"1910.08681","repositories_listed":1,"syntology":null},{"url":"/paper/bobby2-buffer-based-robust-high-speed-object","slug":"bobby2-buffer-based-robust-high-speed-object","title":"BOBBY2: Buffer Based Robust High-Speed Object Tracking","date":"2019-10-18","arxiv_id":"1910.08263","repositories_listed":1,"syntology":null},{"url":"/paper/alignnet-3d-fast-point-cloud-registration-of","slug":"alignnet-3d-fast-point-cloud-registration-of","title":"AlignNet-3D: Fast Point Cloud Registration of Partially Observed Objects","date":"2019-10-10","arxiv_id":"1910.04668","repositories_listed":1,"syntology":null},{"url":"/paper/track-to-reconstruct-and-reconstruct-to-track","slug":"track-to-reconstruct-and-reconstruct-to-track","title":"Track to Reconstruct and Reconstruct to Track","date":"2019-09-30","arxiv_id":"1910.00130","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/track-to-reconstruct-and-reconstruct-to-track#ran","syntology_url":"https://syntology.ai/paper/1910.00130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.00130"}},"official":{"repos":["tobiasfshr/MOTSFusion"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-multi-modality-multi-object-tracking","slug":"robust-multi-modality-multi-object-tracking","title":"Robust Multi-Modality Multi-Object Tracking","date":"2019-09-09","arxiv_id":"1909.03850","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-and-robust-online-learning-for","slug":"discriminative-and-robust-online-learning-for","title":"Discriminative and Robust Online Learning for Siamese Visual Tracking","date":"2019-09-06","arxiv_id":"1909.02959","repositories_listed":1,"syntology":null},{"url":"/paper/multi-target-tracking-by-learning-from","slug":"multi-target-tracking-by-learning-from","title":"Multi Target Tracking by Learning from Generalized Graph Differences","date":"2019-08-19","arxiv_id":"1908.06646","repositories_listed":1,"syntology":null},{"url":"/paper/attentive-deep-regression-networks-for-real","slug":"attentive-deep-regression-networks-for-real","title":"Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance","date":"2019-08-10","arxiv_id":"1908.03812","repositories_listed":1,"syntology":null},{"url":"/paper/boundary-effect-aware-visual-tracking-for-uav","slug":"boundary-effect-aware-visual-tracking-for-uav","title":"Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus Verification","date":"2019-08-10","arxiv_id":"1908.03701","repositories_listed":1,"syntology":null},{"url":"/paper/learning-aberrance-repressed-correlation","slug":"learning-aberrance-repressed-correlation","title":"Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking","date":"2019-08-06","arxiv_id":"1908.02231","repositories_listed":1,"syntology":null},{"url":"/paper/joint-group-feature-selection-and","slug":"joint-group-feature-selection-and","title":"Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking","date":"2019-07-30","arxiv_id":"1907.13242","repositories_listed":1,"syntology":null}],"record_sha256":"481b7f36636b9a374a8da44dd24223a7856da3d6d4b4c6b54e866eded573548b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}