Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving
This addresses real-time object tracking for autonomous vehicles, but it appears incremental as it builds on existing fusion and tracking methods.
The paper tackled multi-object tracking with camera-LiDAR fusion for autonomous driving by proposing an efficient framework with joint detection and tracking, achieving superior performance in accuracy and speed on the KITTI benchmark.
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.