Appearance-based People Recognition by Local Dissimilarity Representations

Among the possible applications of computer vision to video-surveillance, person re-identification over a network of camera sensors, using cues related to clothing appearance, is gaining much interest. Re-identification techniques can be used for various tasks, e.g., online tracking of a person, and off-line retrieval of all video sequences containing an individual of interest, whose image is given as a query. Recently, some authors proposed to exploit clothing appearance descriptors also to retrieve video sequences of individuals that match a textual description of clothing (e.g., 'person wearing a black t-shirt and white trousers'), instead of an image. We name this task 'appearance-based people search'. This functionality can be useful, e.g., in forensics investigations, where a textual description can be provided by a witness. In this paper, we present and experimentally evaluate a general method to perform both person re-identification and people search, using any given descriptor of clothing appearance that exploits widely used multiple part/multiple component representations. It is based on turning the considered appearance descriptor into a dissimilarity-based one, through a framework we previously proposed for speeding up person re-identification methods. Our approach allows one to deploy systems able to perform both tasks with the same pipeline and processing stages for constructing descriptors.