JDT-NAS: Designing Efficient Multi-Object Tracking Architectures for Non-GPU Computers
Dong Chen, Hao Shen, Yuchen Shen · IEEE Transactions on Circuits and Systems for Video Technology · 2023
Recent years have witnessed online multiple object tracking (MOT) revealing its enormous potential in numerous fields. However, existing MOT models are not suitable for deployment and application on non-GPU platforms (e.g. CPU-based computers), due to their inefficient inference. In this paper, we offer an automated approach for designing CPU-efficient multi-object tracking architectures, called JDT-NAS. Specifically, we first design several lightweight basic units as candidate operations to construct a heterogeneous search space for multi-object tracking. Then, based on the predefined space, we develop a four-stage hardware-aware search strategy to achieve the automated design of MOT models. Benefiting from our heterogeneous search space and special search strategy, our JDT-NAS not only allows flexible adjustment of the network scale (including depth, width and resolution dimensions), but also better meets the needs of efficient deployment. Finally, on publicly available standard datasets (e.g. MOT17), we apply JDT-NAS to search for multi-object trackers for fast inference on non-GPU computers. Comprehensive experiments show that these searched MOT architectures achieve superior speed and comparable accuracy compared to their state-of-the-art counterparts. This also indicates that our proposed JDT-NAS is a practical and feasible solution for efficient MOT architecture design.