472 research outputs found

    Gait recognition based on shape and motion analysis of silhouette contours

    Get PDF
    This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subjectā€™s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subjectā€™s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subjectā€™s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subjectā€™s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods

    Key-Pose Prediction in Cyclic Human Motion

    Get PDF
    In this paper we study the problem of estimating innercyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-poses, i.e. distinctive postures of the body. A key-pose is defined by means of only one or two specific features of the complete posture. It is often difficult to detect such subtle features directly. We therefore propose the following method: Given that we observe the swimmer from the side, we build a pictorial structure of poselets to robustly identify random support poses within the regular motion of a swimmer. We formulate a maximum likelihood model which predicts a key-pose given the occurrences of multiple support poses within one stroke. The maximum likelihood can be extended with prior knowledge about the temporal location of a key-pose in order to improve the prediction recall. We experimentally show that our models reliably and robustly detect key-poses with a high precision and that their performance can be improved by extending the framework with additional camera views.Comment: Accepted at WACV 2015, 8 pages, 3 figure

    Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

    Get PDF
    This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

    Gait recognition with shifted energy image and structural feature extraction

    Get PDF
    This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, i.e., the shifted energy image and the gait structural profile, which have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of linear discriminant analysis. In the decision stage, the two representations are fused. Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison with current state-of-the-art systems

    Applications of Factorization Theorem and Ontologies for Activity ModelingRecognition and Anomaly Detection

    Get PDF
    In this thesis two approaches for activity modeling and suspicious activity detection are examined. First is application of factorization theorem extension for deformable models in two dierent contexts. First is human activity detection from joint position information, and second is suspicious activity detection for tarmac security. It is shown that the first basis vector from factorization theorem is good enough to dierentiate activities for human data and to distinguish suspicious activities for tarmac security data. Second approach dierentiates individual components of those activities using semantic methodol- ogy. Although currently mainly used for improving search and information retrieval, we show that ontologies are applicable to video surveillance. We evaluate the domain ontologies from Challenge Project on Video Event Taxonomy sponsored by ARDA from the perspective of general ontology design principles. We also focused on the eect of the domain on the granularity of the ontology for suspicious activity detection

    Gait Recognition: Databases, Representations, and Applications

    No full text
    There has been considerable progress in automatic recognition of people by the way they walk since its inception almost 20 years ago: there is now a plethora of technique and data which continue to show that a personā€™s walking is indeed unique. Gait recognition is a behavioural biometric which is available even at a distance from a camera when other biometrics may be occluded, obscured or suffering from insufficient image resolution (e.g. a blurred face image or a face image occluded by mask). Since gait recognition does not require subject cooperation due to its non-invasive capturing process, it is expected to be applied for criminal investigation from CCTV footages in public and private spaces. This article introduces current progress, a research background, and basic approaches for gait recognition in the first three sections, and two important aspects of gait recognition, the gait databases and gait feature representations are described in the following sections.Publicly available gait databases are essential for benchmarking individual approaches, and such databases should contain a sufficient number of subjects as well as covariate factors to realize statistically reliable performance evaluation and also robust gait recognition. Gait recognition researchers have therefore built such useful gait databases which incorporate subject diversities and/or rich covariate factors.Gait feature representation is also an important aspect for effective and efficient gait recognition. We describe the two main approaches to representation: model-free (appearance-based) approaches and model-based approaches. In particular, silhouette-based model-free approaches predominate in recent studies and many have been proposed and are described in detail.Performance evaluation results of such recent gait feature representations on two of the publicly available gait databases are reported: USF Human ID with rich covariate factors such as views, surface, bag, shoes, time elapse; and OU-ISIR LP with more than 4,000 subjects. Since gait recognition is suitable for criminal investigation applications of the gait recognition to forensics are addressed with real criminal cases in the application section. Finally, several open problems of the gait recognition are discussed to show future research avenues of the gait recognition

    Gait recognition for person re-identification

    Get PDF
    Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset
    • ā€¦
    corecore