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arXiv · 2402.16570

Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking

Wen-Jia Tang·Xiao Liu·Peng Gao·Fei Wang·Ru-Yue Yuan
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Abstract

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.

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Wen-Jia Tang, Xiao Liu, Peng Gao, Fei Wang, Ru-Yue Yuan. 2024-09-30. Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking. https://arxiv.org/abs/2402.16570

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