1.Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Heifei 230601; 2.Key Laboratory of Industrial Image Processing and Analysis of Anhui Province, Science and Technology Department of Anhui Province, Hefei 230039; 3.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190
Abstract The coordinating fusion between modalities is ignored in the existing cross-modal person re-identification methods in the learning process. In this paper, a strategy for cross-modal person re-identification(Re-ID) based on local heterogeneous collaborative dual-path network is proposed. Firstly, the global features of each modality are extracted by the dual-path network for local refinement, and the structured local information of pedestrians is mined. Then, the local information of different modalities is correlated with the label and prediction information to achieve cooperative adaptive fusion and learn more discriminative features. The effectiveness of the proposed method is demonstrated through comprehensive
Fund:Major Project for New Generation of AI(No.2018AAA0100400), National Natural Science Foundation of China(No.61976002), Key Project of Research and Development of Anhui Province(No.201904b11020037), Natural Science Foundation of Anhui Higher Education Institutions of China(No.KJ2019A0033), Open Project of National Laboratory of Pattern Recognition(NLPR)(No.201900046), and Open Fund for Discipline Construction of Institute of Physical Science and Information Technology of Anhui University
Corresponding Authors:JIANG Bo, Ph.D., associate professor. His research interests include image matching and graph matching.
About author:: ZHENG Aihua, Ph.D., associate profe-ssor. Her research interests include person/vehicle re-identification and audio visual computing. ZENG Xiaoqiang, master student. His research interests include cross-modal person re-identification. HUANG Yan, Ph.D., associate professor. His research interests include machine lear-ning and pattern recognition. TANG Jin, Ph.D., professor. His research interests include computer vision and pattern recognition.
ZHENG Aihua,ZENG Xiaoqiang,JIANG Bo等. Cross-Modal Person Re-identification Based on Local Heterogeneous Collaborative Dual-Path Network[J]. , 2020, 33(10): 867-878.
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2020, Vol. 33 
