Transferring clothing parsing from fashion dataset to surveillance

In this paper we address the problem of automatic clothing parsing in surveillance video with the information from user-generated tags such as “jeans” and “T-shirt”. Although clothing parsing has achieved great success in fashion clothing, it is quite challenging to parse clothing in practical surveillance conditions due to complicated environmental interferences, such as illumination change, scale zooming, viewpoint variation and etc. Our method is developed to capture the clothing information from the fashion field and apply it to surveillance domain by weakly-supervised transfer learning. Most of attribute labels in surveillance images convey strong location information, which can be considered as weak labels to deal with the transfer method. Both quantitative and qualitative experiments conducted on practical surveillance datasets have shown the effectiveness of the proposed method.