Person re-identification in the real scene based on the deep learning

• Published in: Artificial life and robotics Vol. 26; no. 4; pp. 396 - 403
• Main Authors: Zhu, Miaomiao, Gong, Shengrong, Qian, Zhenjiang, Serikawa, Seiichi, Zhang, Lifeng
• Format: Journal Article
• Language: English
• Published: Tokyo Springer Japan 01.11.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Cameras; Computation by Abstract Devices; Computer Science; Computer vision; Control; Datasets; Identification; Mechatronics; Object recognition; Optimization; Original Article; Pedestrians; Robotics; Deep learning; Pedestrian detection; Convolutional neural networks; Person re-identification; Real scene
• ISSN: 1433-5298; 1614-7456
• DOI: 10.1007/s10015-021-00689-9

Person re-identification aims at automatically retrieving a person of interest across multiple non-overlapping camera views. Because of increasing demand for real-world applications in intelligent video surveillance, person re-identification has become an important computer vision task and achieved high performance in recent years. However, the traditional person re-identification research mainly focus on matching cropped pedestrian images between queries and candidates on commonly used datasets and divided into two steps: pedestrian detection and person re-identification, there is still a big gap with practical applications. Under the premise of model optimization, based on the existing object detection and person re-identification, this paper achieves a one-step search of the specific pedestrians in the whole images or video sequences in the real scene. The experimental results show that our method is effective in commonly used datasets and has achieved good results in real-world applications, such as finding criminals, cross-camera person tracking, and activity analysis.