PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture
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Chensheng Peng Zhaoyu Zeng Jinling Gao Jundong Zhou Masayoshi Tomizuka Xinbing Wang Chenghu Zhou Nanyang Ye
Multiple object tracking is a critical task in autonomous driving. Existing
works primarily focus on the heuristic design of neural networks to obtain high
accuracy. As tracking accuracy improves, however, neural networks become
increasingly complex, posing challenges for their practical application in real
driving scenarios due to the high level of latency. In this paper, we explore
the use of the neural architecture search (NAS) methods to search for efficient
architectures for tracking, aiming for low real-time latency while maintaining
relatively high accuracy. Another challenge for object tracking is the
unreliability of a single sensor, therefore, we propose a multi-modal framework
to improve the robustness. Experiments demonstrate that our algorithm can run
on edge devices within lower latency constraints, thus greatly reducing the
computational requirements for multi-modal object tracking while keeping lower
latency.