RT Journal Article
T1 Visual Appearance and Soft Biometrics Fusion for Person Re-Identification Using Deep Learning
A1 Khan, Samee Ullah
A1 Khan, Noman
A1 Hussain, Tanveer
A1 Muhammad, Khan
A1 Hijji, Mohammad
A1 Del Ser, Javier
A1 Baik, Sung Wook
AB Learning descriptions of individual pedestrian is a common goal of both person re-identification (P-ReID) and attribute recognition methods, which are typically differentiated only in terms of their granularity. However, existing P-ReID methods only consider identification labels for individual pedestrian. In this article, we present a multi-scale pyramid attention (MSPA) model for P-ReID that jointly manipulates the complementarity between semantic attributes and visual appearance to address this limitation. The proposed MSPA method mainly comprises three steps. Initially, a backbone model followed by appearance and attribute networks is individually trained to perform P-ReID and pedestrian attribute classification tasks. The attribute network primarily focuses on suppressed image areas associated with soft biometric data while retaining the semantic context among attributes using a convolutional long short-term memory architecture. Additionally, the identification network extracts rich contextual features from an image at varying scales using a residual pyramid module. In the second step, the dual network features are fused, and MSPA is re-trained for the P-ReID task to further improve its complementary capabilities. Finally, we experimentally evaluated the proposed model on the two benchmark datasets Market-1501 and DukeMTMC-reID, and the results show that our approach achieved state-of-the-art performance.
SN 1932-4553
YR 2023
FD 2023-05-01
LK https://hdl.handle.net/11556/4916
UL https://hdl.handle.net/11556/4916
LA eng
NO Khan , S U , Khan , N , Hussain , T , Muhammad , K , Hijji , M , Del Ser , J & Baik , S W 2023 , ' Visual Appearance and Soft Biometrics Fusion for Person Re-Identification Using Deep Learning ' , IEEE Journal on Selected Topics in Signal Processing , vol. 17 , no. 3 , pp. 575-586 . https://doi.org/10.1109/JSTSP.2023.3260627
NO Publisher Copyright: © 2023 IEEE.
NO This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government, MSIT, under Grant 2023R1A2C1005788.
DS TECNALIA Publications
RD 28 sept 2024