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Multi-object tracking (MOT) with camera-LiDAR fusion demands accurate resultsof object detection, affinity computation and data association in real time.This paper presents an efficient multi-modal MOT framework with online jointdetection and tracking schemes and robust data association for autonomousdriving applications. The novelty of this work includes: (1) development of anend-to-end deep neural network for joint object detection and correlation using2D and 3D measurements; (2) development of a robust affinity computation moduleto compute occlusion-aware appearance and motion affinities in 3D space; (3)development of a comprehensive data association module for joint optimizationamong detection confidences, affinities and start-end probabilities. Theexperiment results on the KITTI tracking benchmark demonstrate the superiorperformance of the proposed method in terms of both tracking accuracy andprocessing speed.

Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving