Describing Human Identity Using Attributes

Smart surveillance of wide areas requires a system of multiple cameras to keep tracking people by their identities. In such multi-view systems, the captured body figures and appearances of human, the orientation as well as the backgrounds are usually different camera by camera, which brings challenges to the view-invariant representation of human towards correct identification. In order to tackle this problem, we introduce an attribute based description of human identity in this paper. Firstly, two groups of attributes responsible for figure and appearance are obtained respectively. Then, Predict-Taken and Predict-Not-Taken schemes are defined to overcome the attribute-loss problem caused by different view of multi-cameras, and the attribute representation of human is obtained consequently. Thirdly, the human identification based on voter-candidate scheme is carried out by taking into account of human outside of the training data. Experimental results show that our method is robust to view changes, attributes-loss and different backgrounds.