Individual Re-Identification in Blurred Image using SVM/PCA Technique

Abstract

Individual re-identification (Re-ID) is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Pre-id can be considered as a problem of image retrieval. Existing person re-identification methods depend mostly on single-scale appearance information. In this work, to address issues, we demonstrate the benefits of a techniques using Support vector machine or Principal component analysis proposed for pre-id in this study. However, great challenges are being faced in the pre-id task. First, in different scenarios, appearance of the same pedestrian changes dramatically by reason of the body misalignment frequently, various background clutters, large variations of camera views and occlusion. Second, in a public space, different pedestrians wear the same or similar clothes. Therefore, the distinctions between different pedestrian images are subtle. These make the topic of pre-id a huge challenge. The proposed methods are only performed in the training phase and discarded in the testing phase, thus, enhancing the effectiveness of the model. Our model achieves the state-of-theart on the popular benchmark datasets including Market-1501, duke mtmc -re-id and CUHK03. Besides, we conduct a set of ablation experiments to verify the effectiveness of the proposed methods