Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking
<|reference_start|>Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking: Manually-designed network architectures for thermal infrared pedestrian tracking (TIR-PT) require substantial effort from human experts. AlexNet and ResNet are widely used as backbone networks in TIR-PT applications. However, these architectures were originally designed for image classification and object detection tasks, which are less complex than the challenges presented by TIR-PT. This paper makes an early attempt to search an optimal network architecture for TIR-PT automatically, employing single-bottom and dual-bottom cells as basic search units and incorporating eight operation candidates within the search space. To expedite the search process, a random channel selection strategy is employed prior to assessing operation candidates. Classification, batch hard triplet, and center loss are jointly used to retrain the searched architecture. The outcome is a high-performance network architecture that is both parameter- and computation-efficient. Extensive experiments proved the effectiveness of the automated method.<|reference_end|>
@article{tang2024searching,
    title={Searching a Lightweight Network Architecture for Thermal Infrared
  Pedestrian Tracking},
    author={Wen-Jia Tang, Xiao Liu, Peng Gao, Fei Wang, Ru-Yue Yuan},
    journal={arXiv preprint arXiv:2402.16570},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16570},
    primaryClass={cs.CV cs.LG}
}