2021/08/10 by Kemiao Huang, Qi Hao, Huang, Kemiao +2 · 1 citation
Computer Science · #Advanced Neural Network Applications #Video Surveillance and Tracking Methods #Visual Attention and Saliency Detection #cs.CV
paper · pdf · doi:10.48550/arxiv.2108.04602
accepted by IROS 2021
arxiv created 2021/08/10 · arxiv updated 2021/08/11
Multi-object tracking (MOT) with camera-LiDAR fusion demands accurate results of object detection, affinity computation and data association in real time. This paper presents an efficient multi-modal MOT framework with online joint detection and tracking schemes and robust data association for autonomous driving applications. The novelty of this work includes: (1) development of an end-to-end deep neural network for joint object detection and correlation using 2D and 3D measurements; (2) development of a robust affinity computation module to compute occlusion-aware appearance and motion affinities in 3D space; (3) development of a comprehensive data association module for joint optimization among detection confidences, affinities and start-end probabilities. The experiment results on the KITTI tracking benchmark demonstrate the superior performance of the proposed method in terms of both tracking accuracy and processing speed.