Xiuchuan Cheng¹, Meiling Wu¹, Xu Feng¹, Zhiguo Wang², Guisong Liu², Ye Li^2,*

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 >

Xiaoqi Xin^*, Dezhi Han, Mingming Cui

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2407-2425, 2023, DOI:10.32604/cmc.2023.042308 - 29 November 2023

Abstract Person Search is a task involving pedestrian detection and person re-identification, aiming to retrieve person images matching a given objective attribute from a large-scale image library. The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively. The current popular Person Search models, whether end-to-end or two-step, are based on anchor boxes. However, due to the limitations of the anchor itself, the model inevitably has some disadvantages, such as unbalance of positive and negative samples and redundant calculation, which will affect… More >

Cheng Feng^*, Dezhi Han, Chongqing Chen

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 245-261, 2023, DOI:10.32604/cmc.2023.042765 - 31 October 2023

Abstract Person search mainly consists of two submissions, namely Person Detection and Person Re-identification (re-ID). Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network (CNN) (e.g., ResNet). While these structures may detect high-quality bounding boxes, they seem to degrade the performance of re-ID. To address this issue, this paper proposes a Dual-Transformer Head Network (DTHN) for end-to-end person search, which contains two independent Transformer heads, a box head for detecting the bounding box and extracting efficient bounding box feature, and a re-ID head for capturing high-quality re-ID features for the re-ID task. Specifically,… More >