A general method for appearance-based people search based on textual queries

Person re-identification consists of recognising a person appearing in different video sequences, using an image as a query. We propose a general approach to extend appearance-based re-identification systems, enabling also textual queries describing clothing appearance (e.g., “person wearing a white shirt and checked blue shorts”). This functionality can be useful, e.g., in forensic video analysis, when textual descriptions of individuals of interest given by witnesses are available, instead of images. Our approach is based on turning any given appearance descriptor into a dissimilarity-based one. This allows us to build detectors of the clothing characteristics of interest using supervised classifiers trained in a dissimilarity space, independently on the original descriptor. Our approach is evaluated using the descriptors of three different re-identification methods, on a benchmark data se

Person Re-Identification in Distributed Wide-Area Surveillance

Person re-identification (Re-ID) is a fundamental task in automated video surveillance and has been an area of intense research in the past few years. Given an image or video of a person taken from one camera, re-identification is the process of identifying the person from images or videos taken from a different camera. Re-ID is indispensable in establishing consistent labeling across multiple cameras or even within the same camera to re-establish disconnected or lost tracks. Apart from surveillance it has applications in robotics, multimedia, and forensics. Person re-identification is a diffcult problem because of the visual ambiguity and spatio-temporal uncertainty in a person's appearance across different cameras. However, the problem has received significant attention from the computer-vision-research community due to its wide applicability and utility. In this work, we explore the problem of person re-identification for multi-camera tracking, to understand the nature of Re-ID, constraints and conditions under which it is to be addressed and possible solutions to each aspect. We show that Re-ID for multi-camera tracking is inherently an open set Re-ID problem with dynamically evolving gallery and open probe set. We propose multi-feature person models for both single and multi-shot Re-ID with a focus on incorporating unique features suitable for short as well as long period Re-ID. Finally, we adapt a novelty detection technique to address the problem of open set Re-ID. In conclusion we identify the open issues in Re-ID like, long-period Re-ID and scalability along with a discussion on potential directions for further research.Computer Science, Department o