Abstract
Intelligent security expects to avoid the occurrence of robbery, theft, and other undesirable situations through video surveillance. In video surveillance, images of human faces undimmed are not easily available, so pedestrian re-identification (person ReID) is an alternative technique which attracts a mount of researchers attention. Person ReID is a technique used to match pedestrian images across cameras. Due to the interference of shooting angle and camera quality, it is difficult to obtain the images of high resolution, no obstructions, simple backgrounds and similar posture, which brings great challenges to the research of person ReID. Most existing methods of pedestrian re-identification ignore the inconsistency of resolution, and they are based on the assumption that all images have similar and high enough resolution by default. In this paper, we propose a hybrid framework, Super-Recognition of Pedestrian Re-Identification (SRPRID), in order to strengthen pedestrian re-identification based on multi-resolutions images captured by disparate cameras. Particularly, residual dense block (RDB) and Integrated Attention (InnAttn) block are merged to SRPRID. It is worth mentioning that the rank_1 accuracy of our method outperforms the state-of-art method by 17.2 points (86.9% - 69.7%) on CUKH03 dataset of extremely challenging.
Highlights
The gradual growth of urban population has brought great challenges to social security
This paper proposes an end-toend hybrid network Super-Recognition of Pedestrian Re-Identification (SRPRID) shown in Fig. 1 for crossresolution pedestrian weight recognition based on mature super-resolution technology and pedestrian weight recognition technology
The results demonstrate that the proposed SRPRID framework achieves compatitive performance over the existing methods
Summary
The gradual growth of urban population has brought great challenges to social security. In order to ensure the physical security or property security on many public occasions, and prevent or deal with various unsafe incidents in time, the video surveillance network constituted of multiple cameras is deployed in momentous occasions. For a monitoring network complicated and massive image data from monitoring equipment, it is bound to take a lot of time cost only relying on human analysis and processing, and even miss the best opportunity. It is necessary to use computer vision technology for intelligent video surveillance. The best way to identify a target individual of interest is face recognition. Face identification performs person ReID by analyzing the face images captured by cameras.
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