Evaluation of Deep Convolutional Neural Network-Based Representations for Cross Dataset Person Re-Identification

摘要

Video surveillance systems have great importance to ensure public safety. Today, these kind of systems not only capture and distribute video but also have various smart applications. Person re-identification is one of the most important of these applications. In this work, we have exploited deep convolutional neural network-based representations for cross dataset person re-identification problem. We have selected well-known deep convolutional neural network models, namely, AlexNet, VGG-16, and GoogLeNet, and fine-tuned them with the largest publicly available person re-identification datasets. We have employed 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 the VIPeR dataset. Superior results have been obtained with the proposed method compared to the state-of-the-art methods in the field.