Convolutional neural network-based representation for person re-identification
Alper Ulu, Hazım Kemal Ekenel · 2016
In this paper, using a general convolutional neural network (CNN) model, which was developed for object recognition, a successful system has been introduced for the person re-identification problem. To use this CNN model for the person re-identification problem properly, it is individually fine-tuned using different body parts of person images. For feature extraction, we used the seventh layer of the CNN model, which was re-trained with the available datasets. Then, we used cosine similarity metric to calculate the similarity between extracted features. CUHK03 and Market-1501 datasets were used as the training sets and the proposed method has been tested on VIPeR dataset. Superior results have been obtained with the proposed method, compared to the state-of-the-art methods in the field.