Current person re-identification (ReID) methods typically rely on single-frame imagery features, whilst ignoring space-time information from image sequences often available in the practical surveillance scenarios. Single-frame (single-shot) based visual appearance matching is inherently limited for person ReID in public spaces due to the challenging visual ambiguity and uncertainty arising from non-overlapping camera views where viewing condition changes can cause significant people appearance variations. In this work, we present a novel model to automatically select the most discriminative video fragments from noisy incomplete image sequences of people from which reliable space-time and appearance features can be computed, whilst simultaneously learning a video ranking function for person ReID. Using the PRID @math , iLIDS-VID, and HDA+ image sequence 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 single- multi-shot ReID methods.
Space-time information of sequences has been extensively exploited by gait recognition @cite_23 @cite_32 @cite_53 @cite_33 . However, these methods often make stringent assumptions on the image sequences, e.g. uncluttered background, consistent silhouette extraction and alignment, accurate gait phase estimation and complete gait cycles, most of which are unrealistic in ordinary person ReID scenarios. It is challenging to extract a suitable gait representation from typical ReID data. In contrast, our approach relaxes significantly these assumptions by simultaneously selecting discriminative video fragments from noisy sequences, learning and matching them without temporal alignment.
@cite_53 @cite_33 @cite_32 @cite_23
2126680226 116734009 2151458682
In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches The advantage of gait over other biometrics such as face or fingerprint is that it can operate from a distance and without subject cooperation. However, this also makes gait subject to changes in various covariate conditions including carrying, clothing, surface and view angle. Existing approaches attempt to address these condition changes by feature selection, feature transformation or discriminant subspace learning. However, they suffer from lack of training samples from each subject, can only cope with changes in a subset of conditions with limited success, and are based on the invalid assumption that the covariate conditions are known a priori. They are thus unable to perform gait recognition under a genuine uncooperative setting. We propose a novel approach which casts gait recognition as a bipartite ranking problem and leverages training samples from different classes people and even from different datasets. This makes our approach suitable for recognition under a genuine uncooperative setting and robust against any covariate types, as demonstrated by our extensive experiments. Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the humanlD gait challenge problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 gigabytes of data). This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The experimental results are presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology.