The OU-ISIR Gait Database Comprising the Treadmill Dataset

Pattern Recognition Letters

2016

This paper proposes a gait recognition method which is invariant to maximum number of challenging factors of gait recognition mainly unpredictable variation in clothing and carrying conditions. The method introduces an averaged gait key-phase image (AGKI) which is computed by averaging each of the five key-phases of the gait periods of a gait sequence. It analyses the AGKIs using high-pass and low-pass Gaussian filters, each at three cut-off frequencies to achieve robustness against unpredictable variation in clothing and carrying conditions in addition to other covariate factors, e.g., walking speed, segmentation noise, shadows under feet and change in hair style and ground surface. The optimal cut-off frequencies of the Gaussian filters are determined based on an analysis of the focus values of filtered human subject's silhouettes. The method applies rotation forest ensemble learning recognition to enhance both individual accuracy and diversity within the ensemble for improved identification rate. Extensive experiments on public datasets demonstrate the efficacy of the proposed method.

“…The proposed method is evaluated using two public datasets: USF HumanID gait challenge dataset [22] and OU-ISIR treadmill gait dataset B [18].…”

Section: Methodsmentioning

confidence: 99%

Section: Cut-off Frequency Selectionmentioning

confidence: 99%

Section: Ou-isir Treadmill Gait Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Clothing and carrying condition invariant gait recognition based on rotation forest

Pattern Recognition Letters

2016

“…Therefore, VI-MGR is evaluated using several experimental setups to match with the experimental setup of different methods for uniform comparison with their reported results on three publicly available gait datasets: CASIA B gait dataset [44], USF HumanID gait challenge dataset [2] and OU-ISIR treadmill gait dataset B [45]. VI-MGR is compared with the following methods: canonical correlation analysis (CCA) based method [21], joint's position estimation and viewpoint rectification (JPE-VR) based method [18], chrono-gait image (CGI) based method [5], gait energy image (GEI) based method [3], radial integration transform, circular integration transform and weighted Krawtchouk moments (RCK-G) based method [29], gait flow image (GFI) based method [6], method using matrixbased marginal Fisher analysis (MMFA) [13], general tensor discriminant analysis and Gabor features (GTDA-GF) based method [14], dynamics normalisation based gait recognition (DNGR) method [4], spatio-temporal motion characteristics, statistical and physical parameters (STM-SPP) based method [26], spatio-temporal shape and dynamic motion (STS-DM) analysis based method [27] and Gabor wavelet and patch distribution feature (PDF) based method (GPDF) [7].…”

Section: Methodsmentioning

confidence: 99%

Robust view-invariant multiscale gait recognition

2015

Pattern Recognition

The paper proposes a two-phase view-invariant multiscale gait recognition method (VI-MGR) which is robust to variation in clothing and presence of a carried item. In phase 1, VI-MGR uses the entropy of the limb region of a gait energy image (GEI) to determine the matching gallery view of the probe using 2-dimensional principal component analysis and Euclidean distance classifier. In phase 2, the probe subject is compared with the matching view of the gallery subjects using multiscale shape analysis. In this phase, VI-MGR applies Gaussian filter to a GEI to generate a multiscale gait image for gradually highlighting the subject's inner shape characteristics to achieve insensitiveness to boundary shape alterations due to carrying conditions and clothing variation. A weighted random subspace learning based classification is used to exploit the high dimensionality of the feature space for improved identification by avoiding overlearning. Experimental analyses on public datasets demonstrate the efficacy of VI-MGR.

“…Figure 12: Comparison with related works. Baseline [15], CMU, DNGR [13] and STS-DM are evaluated on CMU MoBo gait data set (experiment 2 of CMU) with walking speed variation of 3.3 km/h and 4.5 km/h, while ST-WS [36] and SI-PSA [35] are evaluated on OU-ISIR treadmill gait data set A [56] with walking speed variation of 3 km/h and 4 km/h between gallery and probe gait sequences.…”

Section: Comparisonsmentioning

confidence: 99%

Gait recognition based on shape and motion analysis of silhouette contours

Computer Vision and Image Understanding

2013

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject'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 subject'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 subject'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 subject'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.