Abstract (English): |
Objective Person re-identification is an extremely challenging problem and has practical application value. It plays an important role in video surveillance systems because it can reduce human efforts in searching for a target from a large number of videos. This topic has gained increasing interest in computer vision. Nowadays, person re-identification algorithms have been applied in criminal investigation, where the interference of passers-by can be eliminated to help the police find final suspects. However, differences in color, illumination, posture, imaging quality, as well as low-resolution of the captured frames cause large appearance variance across multiple cameras; thus, person re-identification remains a significant problem. An algorithm for person re-identification, which is based on multi-feature fusion and alternating direction method of multipliers, is proposed to improve the accuracy of person re-identification. Method First, the original images are processed by the image enhancement algorithm to reduce the impact of illumination changes. This enhancement algorithm is committed to provide an image that is close to human visual characteristics. Then, the method of non-uniform segmentation that processes images is used. The method uses a sub-window size of 10-by-10 pixels with 5-pixel overlapping steps to obtain the local information of the pedestrian image. Meanwhile, the method uses the specific region mean method to divide the pedestrian image into five blocks. Specifically, depending on the difference of the expression ability of the legs and torso, these parts are divided into three blocks and two blocks, respectively. Then, the second and third blocks take the maximum operation, whereas the other blocks perform the mean operation because the second and third blocks are less affected by ambient noise compared with the other blocks. We also extract the HSV and LAB color features of the processed images, a texture feature of scale-invariant local ternary pattern and a shape feature of histogram of oriented gradient. The existing pedestrian re-identification algorithms generally consider the matching between local regions to eliminate the gap information between blocks. The combination of the global and local methods can effectively solve this problem. The proposed algorithm uses the multi-feature fusion method to combine the global and local information, which combines the global and local similarity measurement function of the related person, to obtain the final similarity function. Finally, the optimal distance measurement matrix is updated by the alternating direction method of multipliers, and the final similarities between each pair are obtained to conduct the re-identification. Result The proposed method is demonstrated on four public benchmark datasets including VIPeR, CUHK01, CUHK03, and GRID. Each dataset has its own characteristics. The proposed method achieves a 51.5% rank 1 (represents the accurately matched pair) on VIPeR benchmark and 48.7% and 21.4% on CUHK01 and GRID benchmarks, respectively. Rank 5 (represents the expectation of the matches at rank 5) is more than 80% on the VIPeR datasets and more than 70% on the CUHK01 datasets. The proposed method achieved 62.40% and 55.05% rank 1 identification rates with the labeled bounding boxes and automatically detected bounding boxes, respectively, thereby indicating that the method outperforms that of local maximal occurrence with an improvement of 10.2% for the labeled setting and 8.8% for the detected setting. The proposed method significantly improves the recognition rate and has a practical application value. Conclusion The experimental results show that the proposed method can express the image information of pedestrians effectively. Furthermore, the effectiveness of our algorithm stems from the non-uniform segmentation and the specific mean method, which reduces the influence of ambient noise, increases robustness to occlusion, and is more flexible in handling pose variation. The updated distance measure matrix can express the information of the distance between pedestrians and improve the recognition rate effectively. This method is applicable to person re-identification in most scenarios, especially for static image-based person re-identification in complex scenes. This method can maintain high recognition accuracy even in the presence of local occlusion, illumination difference, and pose or viewpoint difference. [ABSTRACT FROM AUTHOR] |