{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/multi-camera-trajectory-matching-based-on","title":"Multi‑camera trajectory matching based on hierarchical clustering and constraints","arxiv_id":null,"date":"2023-10-19","proceeding":"Multimedia Tools and Applications 2023 10","authors":["Gábor Szűcs","Regő Borsodi","Dávid Papp"],"abstract":"The fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking. Autonomous driving and traffic monitoring systems, especially the on-premise installed fixed position multi-camera configurations, benefit greatly from recent advances. In this paper, we propose a Multi-Camera Multi-Target (MCMT) vehicle tracking system using a constrained hierarchical clustering solution, which improves trajectory matching, and thus provides a\r\nmore robust tracking of objects transitioning between cameras. YOLOv5, ByteTrack, and ResNet50-IBN ReID networks are used for vehicle detection and tracking. Static attributes such as vehicle type and vehicle color are determined from ReID features with SVM. The proposed ReID feature-based attribute categorization shows better performance, than its pure CNN counterpart. Single-camera trajectories (SCTs) are combined into multi-camera trajectories (MCTs) using hierarchical agglomerative clustering (HAC) with time and space constraints (our proposed algorithm is denoted by MCT#MAC). Similarities between SCTs are measured by comparing the mean ReID features cumulated on the trajectory. The system was evaluated on more datasets, and our experiments demonstrate that constraining HAC by manipulating the proximity matrix greatly improves the multi-camera IDF1 score.","url_abs":"https://doi.org/10.1007/s11042-023-17397-0","url_pdf":"https://doi.org/10.1007/s11042-023-17397-0","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-camera-trajectory-matching-based-on","repo_url":"https://github.com/regob/vehicle_mtmc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"constrained-clustering","task_name":"Constrained Clustering"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"trajectory-clustering","task_name":"Trajectory Clustering"},{"task_slug":"unsupervised-vehicle-re-identification","task_name":"Unsupervised Vehicle Re-Identification"},{"task_slug":"vehicle-re-identification","task_name":"Vehicle Re-Identification"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"object-detection-1","task_name":"object-detection"},{"task_slug":"vehicle-detection","task_name":"vehicle detection"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"ssd","method_name":"SSD"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/vehicle-re-identification-on-veri-wild-large","task":"Vehicle Re-Identification","dataset":"VeRi-Wild Large","model":"ResNet50-IBN","rank_in_archive_order":2,"of":2,"metrics":{"mAP":"58.62"},"uses_additional_data":false},{"leaderboard":"/sota/vehicle-re-identification-on-veri-wild-small","task":"Vehicle Re-Identification","dataset":"VeRi-Wild Small","model":"ResNet50-IBN","rank_in_archive_order":3,"of":3,"metrics":{"mAP":"73.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}