Gait Image Sequences Research Articles

Development of a screening AI model for Parkinson's disease using gait image sequences

Development of a screening AI model for Parkinson's disease using gait image sequences

Athlete Gait Feature Recognition Method Based on Multisource Sensing Information

Aiming at the problem of low accuracy of two-dimensional gait recognition at present, a gait feature recognition method based on multisource sensing information is proposed. The multisource sensing information is combined to collect the athlete’s gait characteristics, collect the single frame gait image sequence of the human lower limbs during the movement, and extract the human body’s three-dimensional feature data during human walking by using the body structure and multisource sensing information, so as to realize the separation of the athlete’s gait image background. Finally, it is confirmed by experiments that the recognition rate of athlete gait feature recognition method based on multisource sensing information is significantly improved.

Spatio-temporal silhouette sequence reconstruction for gait recognition against occlusion

Gait-based features provide the potential for a subject to be recognized even from a low-resolution image sequence, and they can be captured at a distance without the subject’s cooperation. Person recognition using gait-based features (gait recognition) is a promising real-life application. However, several body parts of the subjects are often occluded because of beams, pillars, cars and trees, or another walking person. Therefore, gait-based features are not applicable to approaches that require an unoccluded gait image sequence. Occlusion handling is a challenging but important issue for gait recognition. In this paper, we propose silhouette sequence reconstruction from an occluded sequence (sVideo) based on a conditional deep generative adversarial network (GAN). From the reconstructed sequence, we estimate the gait cycle and extract the gait features from a one gait cycle image sequence. To regularize the training of the proposed generative network, we use adversarial loss based on triplet hinge loss incorporating Wasserstein GAN (WGAN-hinge). To the best of our knowledge, WGAN-hinge is the first adversarial loss that supervises the generator network during training by incorporating pairwise similarity ranking information. The proposed approach was evaluated on multiple challenging occlusion patterns. The experimental results demonstrate that the proposed approach outperforms the existing state-of-the-art benchmarks.

Color-mapped contour gait image for cross-view gait recognition using deep convolutional neural network

In recent decades, gait recognition has garnered a lot of attention from the researchers in the IT era. Gait recognition signifies verifying or identifying the individuals by their walking style. Gait supports in surveillance system by identifying people when they are at a distance from the camera and can be used in numerous computer vision and surveillance applications. This paper proposes a stupendous Color-mapped Contour Gait Image (CCGI) for varying factors of Cross-View Gait Recognition (CVGR). The first contour in each gait image sequence is extracted using a Combination of Receptive Fields (CORF) contour tracing algorithm which extracts the contour image using Difference of Gaussians (DoG) and hysteresis thresholding. Moreover, hysteresis thresholding detects the weak edges from the total pixel information and provides more well-balanced smooth features compared to an absolute one. Second CCGI encodes the spatial and temporal information via color mapping to attain the regularized contour images with fewer outliers. Based on the front view of a human walking pattern, the appearance of cross-view variations would reduce drastically with respect to a change of view angles. This proposed work evaluates the performance analysis of CVGR using Deep Convolutional Neural Network (CNN) framework. CCGI is considered a gait feature for comparing and evaluating the robustness of our proposed model. Experiments conducted on CASIA-B database show the comparisons of previous methods with the proposed method and achieved 94.65% accuracy with a better recognition rate.

Predicting Three-Dimensional Gait Parameters with a Single Camera Video Sequence

Human gait reflects biomedical conditions and thus can potentially be used for identification. With the increasing utility of CCTVs for surveillance, there have been various attempts to recognize persons using gait image sequences from a single camera. We investigated the accuracy of estimating body segment lengths and joint angles during gait calculated from a video sequence using a gait database. We recruited 30 subjects and collected motion capture data during walking and extracted the trajectories of 17 body points. Principal component analysis (PCA) was applied to the collected gait. We implemented full gait cycle-based (FGC) PCA and gait-phase-specific (GPS) PCA. Three-dimensional poses were estimated from gait event frames using FGC-PCA and GPS-PCA. The estimated poses in discrete gait event frames were interpolated to estimate motion during a full gait cycle. The body pose from GPSPCA was less sensitive to camera angles and smaller errors compared to FGC-PCA. The segment lengths of the upper arm (r=0.79), lower arm (r=0.63), upper leg (r=0.86), and lower leg (r=0.81) were highly correlated with the lengths obtained from the motion capture data. Three-dimensionally reconstructed human motion can reveal personal biometric information and has the potential to be used for human identification.

View Transformation Model Incorporating Quality Measures for Cross-View Gait Recognition.

Cross-view gait recognition authenticates a person using a pair of gait image sequences with different observation views. View difference causes degradation of gait recognition accuracy, and so several solutions have been proposed to suppress this degradation. One useful solution is to apply a view transformation model (VTM) that encodes a joint subspace of multiview gait features trained with auxiliary data from multiple training subjects, who are different from test subjects (recognition targets). In the VTM framework, a gait feature with a destination view is generated from that with a source view by estimating a vector on the trained joint subspace, and gait features with the same destination view are compared for recognition. Although this framework improves recognition accuracy as a whole, the fit of the VTM depends on a given gait feature pair, and causes an inhomogeneously biased dissimilarity score. Because it is well known that normalization of such inhomogeneously biased scores improves recognition accuracy in general, we therefore propose a VTM incorporating a score normalization framework with quality measures that encode the degree of the bias. From a pair of gait features, we calculate two quality measures, and use them to calculate the posterior probability that both gait features originate from the same subjects together with the biased dissimilarity score. The proposed method was evaluated against two gait datasets, a large population gait dataset of over-ground walking (course dataset) and a treadmill gait dataset. The experimental results show that incorporating the quality measures contributes to accuracy improvement in many cross-view settings.

Gait recognition by fluctuations

This paper describes a method of gait recognition by suppressing and using gait fluctuations. Inconsistent phasing between a matching pair of gait image sequences because of temporal fluctuations degrades the performance of gait recognition. We remove the temporal fluctuations by generating a phase-normalized gait image sequence with equal phase intervals. If inter-period gait fluctuations within a gait image sequence are repeatedly observed for the same subject, they can be regarded as a useful distinguishing gait feature. We extract phase fluctuations as temporal fluctuations as well as gait fluctuation image and trajectory fluctuations as spatial fluctuations. We combine them with the matching score using the phase-normalized image sequence as additional matching scores in the score-level fusion framework or as quality measures in the score-normalization framework. We evaluated the methods in experiments using large-scale publicly available databases and showed the effectiveness of the proposed methods.

A framework for online gait recognition based on multilinear tensor analysis

The gait recognition is to recognize an individual based on the characteristics extracted from the gait image sequence. There are many researches for the gait recognition which use diverse kinds of information such as shape of gait silhouette, motion variation caused by walking, and so on. In general, shape information is more useful for recognition. However, shape information is influenced by a variety of factors, which degrade the recognition performance. Moreover, the information used in most of those studies might be able to be extracted after all of one or more sequences of the gait cycle are known. And it is also hard to discriminate the gait cycle from given gait sequences exactly by the online approach. In regard to these difficulties, we propose a novel gait recognition method based on the multilinear tensor analysis. To recognize the cyclic characteristic of gait without an exact division for the gait cycle, this paper's propose is the method to form the accumulated silhouette and then describes those as the tensor. For the accumulated silhouette proposed by this paper, the image sequence of one gait cycle is divided into four sections in the training phase. However, discrimination for the gait cycle in the training phase is not directly related to the recognition phase, thus the online approach is possible. We first form the accumulated silhouettes for every individual using gait silhouettes within each section. And then, we represent these accumulated silhouettes as the tensor. Using a multilinear tensor analysis, we compute the core tensor which governs the interaction between factors organizing the original tensor, and then compose the basis to recognize the individual in the online recognition framework. Finally, we recognize the individual using the computation of similarity based on the Euclidean distance, which is more suitable to our method. We verify the superiority of the proposed approach via experiments with real gait sequences.

A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences

Parkinson’s Disease (PD) is a common neurodegenerative disorder with progressive loss of dopaminergic and other sub-cortical neurons. Among various approaches, gait analysis is commonly used to help identify the biometric features of PD. There have been some studies to date on both the classification of PD and estimation of gait parameters. However, it is also important to construct a regression system that can evaluate the degree of abnormality in PD patients. In this paper, we intended to develop a PD gait regression model that is capable of predicting the severity of motor dysfunction from given gait image sequences. We used a model-free strategy and thus avoided the critical demands of segmentation and parameter estimation. Furthermore, we used linear discriminant analysis (LDA) to increase the feature efficiency by maximizing and minimizing the between- and within-group variations. Regression was also achieved by assessing the spatial and temporal information through classification and finally by using these two new indices for linear regression. According to the experiments, the outcomes significantly correlated with the sum of sub-scores from the Unified Parkinson’s Disease Rating Scale (UPDRS): motor examination section with r = 0.92 and 0.85 for training and testing, respectively, with p < 0.0001. Compared with conventional methods, our system provided a better evaluation of PD abnormality.

Gait Recognition Based on Outermost Contour

Gait recognition aims to identify people by the way they walk. In this paper, a simple but effective gait recognition method based on outermost contour is proposed. For each gait image sequence, an adaptive silhouette extraction algorithm is firstly used to segment the frames of the sequence and a series of post-processing is applied to obtain the normalized silhouette images with less noise. Then a novel feature extraction method based on outermost contour is performed. Principal Component Analysis (PCA) is adopted to reduce the dimensionality of the distance signals derived from the outermost contours of silhouette images. Then Multiple Discriminant Analysis (MDA) is used to optimize the separability of gait features belonging to different classes. Nearest Neighbor (NN) classifier and Nearest Neighbor classifier with respect to class Exemplars (ENN) are used to classify the final feature vectors produced by MDA. In order to verify the effectiveness and robustness of our feature extraction algorithm, we ...