Adversarial Multi Scale Features Learning for Person Re Identification

Person 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. Pried 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 deep model with Multi scale Feature Representation Learning MFRL using Convolutional Neural Networks CNN and Random Batch Feature Mask RBFM is proposed for pre id in this study. The RBFM is enlightened by the drop block and Batch Drop Block BDB dropout based approaches. 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 the art 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. Mrs. D. Radhika | D. Harini | N. Kirujha | Dr. M. Duraipandiyan | M. Kavya "Adversarial Multi-Scale Features Learning for Person Re-Identification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42562.pdf Paper URL: https://www.ijtsrd.comengineering/computer-engineering/42562/adversarial-multiscale-features-learning-for-person-reidentification/mrs-d-radhika