Xiuchuan Cheng1, Meiling Wu1, Xu Feng1, Zhiguo Wang2, Guisong Liu2, Ye Li2,*
CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3527-3549, 2025, DOI:10.32604/cmc.2025.067264
- 23 September 2025
Abstract The requirement for precise detection and recognition of target pedestrians in unprocessed real-world imagery drives the formulation of person search as an integrated technological framework that unifies pedestrian detection and person re-identification (Re-ID). However, the inherent discrepancy between the optimization objectives of coarse-grained localization in pedestrian detection and fine-grained discriminative learning in Re-ID, combined with the substantial performance degradation of Re-ID during joint training caused by the Faster R-CNN-based branch, collectively constitutes a critical bottleneck for person search. In this work, we propose a cascaded person search model (SeqXt) based on SeqNet and ConvNeXt that… More >
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