# Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking

Source: [Searching a Lightweight Network Architecture for Thermal Infrared Pedestrian Tracking](<https://www.sota2.com/research/paper/68fa74f71e209131cea64e3e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:09.782Z

Authors: Wen-Jia Tang, Xiao Liu, Peng Gao, Fei Wang, Ru-Yue Yuan

Published: 2024-02-26T13:48:44.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2402.16570>)

[PDF](<https://arxiv.org/pdf/2402.16570.pdf>)