Evrişimli sinir ağları kullanarak kişiyi yeniden tanımlama

Person re-identification is an important computer vision topic particularly for surveillance system applications. The main aim of person re-identification is to identify previously observed individuals over a camera network with nonoverlapping occurrences. Various approaches have been introduced to overcome this problem. This study has two distinct key contributions. First, a new part-based method for person re-identification, which combines semantically partitioned body part masks with a convolutional neural network, is proposed. With this study, it is observed that the proposed body part-based system has promising results, and has advantages over the baseline person re-identification systems. The second contribution of this study is to investigate and compare potential lightweight convolutional neural networks suitable for person re-identification task. Results show that some lightweight convolutional neural networks can be used instead of more generalized deeper networks. Well-designed lightweight convolutional neural networks may have higher accuracy with lower computational cost for person reidentification.