Person Re-identification by Video Ranking

Abstract. Current person re-identification (re-id) methods typically rely on single-frame imagery features, and ignore space-time information from image sequences. Single-frame (single-shot) visual appearance matching is inherently limited for person re-id in public spaces due to visual ambiguity arising from non-overlapping camera views where viewpoint and lighting changes can cause significant appearance variation. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy image sequences of people where more reliable space-time features can be extracted, whilst simultaneously to learn a video ranking function for person re-id. Also, we introduce a new image sequence re-id dataset (iLIDS-VID) based on the i-LIDS MCT benchmark data. Using the iLIDS-VID and PRID 2011 sequence re-id datasets, we extensively conducted comparative evaluations to demonstrate the advantages of the proposed model over contemporary gait recognition, holistic image sequence matching and state-of-the-art singleshot/multi-shot based re-id methods.

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