Searching a Lightweight Network Architecture for Thermal Infrared
  Pedestrian Tracking
Peng Gao Xiao Liu Yu Wang Ru-Yue Yuan
Manually-designed network architectures for thermal infrared pedestrian
tracking (TIR-PT) require substantial effort from human experts. Neural
networks with ResNet backbones are popular for TIR-PT. However, TIR-PT is a
tracking task and more challenging than classification and detection. 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.