2019/07/25 by Michael Motro, Joydeep Ghosh, Motro, Michael +1
Computer Science · Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Robotics (cs.RO) #Target Tracking and Data Fusion in Sensor Networks #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1907.11306
openalex publication_date 2019/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Autonomous vehicles often perceive the environment by feeding sensor data to a learned detector algorithm, then feeding detections to a multi-object tracker that models object motions over time. Probabilistic models of multi-object trackers typically assume that errors in the detector algorithm occur randomly over time. We instead assume that undetected objects and false detections will persist in certain conditions, and modify the tracking framework to account for them. The modifications are tested on a vehicle tracking dataset using a state-of-the-art lidar-based detector, a novel lightweight detector, and a fusion of camera and lidar detectors. For each detector, the persistence modifications notably improve performance and enable the model to outperform baseline trackers.