{"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/no-blind-spots-full-surround-multi-object","title":"No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs","arxiv_id":"1802.08755","date":"2018-02-23","proceeding":null,"authors":["Akshay Rangesh","Mohan M. Trivedi"],"abstract":"Online multi-object tracking (MOT) is extremely important for high-level\nspatial reasoning and path planning for autonomous and highly-automated\nvehicles. In this paper, we present a modular framework for tracking multiple\nobjects (vehicles), capable of accepting object proposals from different sensor\nmodalities (vision and range) and a variable number of sensors, to produce\ncontinuous object tracks. This work is a generalization of the MDP framework\nfor MOT, with some key extensions - First, we track objects across multiple\ncameras and across different sensor modalities. This is done by fusing object\nproposals across sensors accurately and efficiently. Second, the objects of\ninterest (targets) are tracked directly in the real world. This is a departure\nfrom traditional techniques where objects are simply tracked in the image\nplane. Doing so allows the tracks to be readily used by an autonomous agent for\nnavigation and related tasks.\n  To verify the effectiveness of our approach, we test it on real world highway\ndata collected from a heavily sensorized testbed capable of capturing\nfull-surround information. We demonstrate that our framework is well-suited to\ntrack objects through entire maneuvers around the ego-vehicle, some of which\ntake more than a few minutes to complete. We also leverage the modularity of\nour approach by comparing the effects of including/excluding different sensors,\nchanging the total number of sensors, and the quality of object proposals on\nthe final tracking result.","url_abs":"http://arxiv.org/abs/1802.08755v4","url_pdf":"http://arxiv.org/pdf/1802.08755v4.pdf","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":"no-blind-spots-full-surround-multi-object","repo_url":"https://github.com/dannofield/CarND-Extended-Kalman-Filter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"no-blind-spots-full-surround-multi-object","repo_url":"https://github.com/ken-power/SelfDrivingCarND-ExtendedKalmanFilter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"no-blind-spots-full-surround-multi-object","repo_url":"https://github.com/ken-power/SensorFusionND-3D-Object-Tracking","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"multi-object-tracking","task_name":"Multi-Object Tracking"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"online-multi-object-tracking","task_name":"Online Multi-Object Tracking"},{"task_slug":"spatial-reasoning","task_name":"Spatial Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.08755","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}