Semi-automatic Data Annotation Tool for Person Re-identification Across Multi Cameras

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. It is becoming a hot research topic due to its value in both machine learning research and video surveillance applications. Considering the current success of deep learning, having tons of person images with identity labels are important and helpful for learning effective person matchers. However, collecting labeled images for person re-identification is more difficult than other similar tasks such as face recognition due to complex intra-class variations in illumination, pose, viewpoint, blur, low resolution, and occlusion. Although the volume of surveillance videos has become larger and larger today, it is time-consuming and costs lots of human labors in labeling a large dataset for person re-identification. In this paper, we propose a semi-automatic data annotation tool to accelerate annotation of person images across multi cameras. This tool consists of automatic person detection and tracking algorithms for person image collection, and an ad-hoc person matcher for automatic person matching suggestions across multi cameras. Moreover, we further utilize background and video sequence information for identity confirmation during annotation, which is also a good intuition for the future design of person re-identification algorithms.