Person Re-Identification with Deep Features and Transfer Learning

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification:feature representation and metric learning. At present, there are many methods in the study of person re-identification, which has achieved remarkable results. Due to the difference of the data distribution in different scenarios, the performance of the person re-identification in the new scene is significantly decreased. In order to avoid the tedious manual annotation, and to make full use of the original detector and labeled samples, the research of person re-identification based on transfer learning has received more and more attention. Existing approaches adopt a fixed metric for matching all the subjects. In this work, we propose a Feature Net (FN) architecture with Convolution Neural Networks (CNNs) to learn the pedestrian feature, reserved more useful information. And use Cosine distance to measure the each image pair's similarity directly which is more efficient but uncomplicated than others. Our method can be applied to different scenarios and improved the recognition performance. Experiments on the challenging datasets show the effectiveness of our methods, especially on cuhk03 dataset, we achieve the state-of-the-art result.