  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.
