Video from nearly still: An application to low frame-rate gait recognition

In this paper, we propose a temporal super resolution ap-proach for quasi-periodic image sequence such as human gait. The proposed method effectively combines example-based and reconstruction-based temporal super resolution approaches. A periodic image sequence is expressed as a manifold parameterized by a phase and a standard mani-fold is learned from multiple high frame-rate sequences in the training stage. In the test stage, an initial phase for each frame of an input low frame-rate image sequence is estimated based on the standard manifold at first, and the manifold reconstruction and the phase estimation are then iterated to generate better high frame-rate images in the energy minimization framework that ensures the fitness to both the input images and the standard manifold. The pro-posed method is applied to low frame-rate gait recognition and experiments with real data of 100 subjects demonstrate a significant improvement by the proposed method, particu-larly for quite low frame-rate videos (e.g., 1 fps). 1

Gait Identification Considering Body Tilt byWalking Direction Changes

Gait identification has recently gained attention as a method of identifying individuals at a distance. Thought most of the previous works mainly treated straight-walk sequences for simplicity, curved-walk sequences should be also treated considering situations where a person walks along a curved path or enters a building from a sidewalk. In such cases, person's body sometimes tilts by centrifugal force when walking directions change, and this body tilt considerably degrades gait silhouette and identification performance, especially for widely-used appearance-based approaches. Therefore, we propose a method of body-tilted silhouette correction based on centrifugal force estimation from walking trajectories. Then, gait identification process including gait feature extraction in the frequency domain and learning of a View Transformation Model (VTM) follows the silhouette correction. Experiments of gait identification for circular-walk sequences demonstrate the effectiveness of the proposed method

タイワ ヲ モチイタ ブッタイ ニンシキ ニ カンスル ケンキュウ

To construct in situ velocity-porosity relationships for oceanic basalt, considering crack features, P- and S-wave velocity measurements on basaltic samples obtained from the eastern flank of the Juan de Fuca Ridge were carried out under confining pressures up to 40 MPa. Assuming that the changes in velocities with confining pressures are originated by micro-crack closure, we estimated micro-crack aspect ratio spectra using the Kuster-Toksöz theory. The result demonstrates that the normalised aspect ratio spectra of the different samples have similar characteristics. From the normalised aspect ratio spectrum, we then constructed theoretical velocity-porosity relationships by calculating an aspect ratio spectrum for each porosity. In addition, by considering micro-crack closure due to confining pressure, a velocity-porosity relationship as a function of confining pressure could be obtained. The theoretical relationships that take into account the aspect ratio spectra are consistent with the observed relationships for over 100 discrete samples measured at atmospheric pressure, and the commonly observed pressure dependent relationships for a wide porosity range. The agreement between the laboratory-derived data and theoretically estimated values demonstrates that the velocity-porosity relationships of the basaltic samples obtained from the eastern flank of the Juan de Fuca Ridge, and their pressure dependence, can be described by the crack features (i.e. normalised aspect ratio spectra) and crack closure

Gait Recognition: Databases, Representations, and Applications

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 person’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

Human tracking and segmentation supported by silhouette-based gait recognition

Abstract — Gait recognition has recently gained attention as an effective approach to identify individuals at a distance from a camera. Most existing gait recognition algorithms assume that people have been tracked and silhouettes have been segmented successfully. Tacking and segmentation are, however, very difficult especially for articulated objects such as human beings. Therefore, we present an integrated algorithm for tracking and segmentation supported by gait recognition. After the tracking module produces initial results consisting of bounding boxes and foreground likelihood images, the gait recognition module searches for the optimal silhouette-based gait models corresponding to the results. Then, the segmentation module tries to segment people out using the provided gait silhouette sequence as shape priors. Experiments on real video sequences show the effectiveness of the proposed approach. I

Gait Recognition Using Period-Based Phase Synchronization for Low Frame-Rate Videos

Abstract—This paper proposes a method for period-based gait trajectory matching in the eigenspace using phase syn-chronization for low frame-rate videos. First, a gait period is detected by maximizing the normalized autocorrelation of the gait silhouette sequence for the temporal axis. Next, a gait silhouette sequence is expressed as a trajectory in the eigenspace and the gait phase is synchronized by time stretching and time shifting of the trajectory based on the detected period. In addition, multiple period-based matching results are integrated via statistical procedures for more robust matching in the presence of fluctuations among gait sequences. Results of experiments conducted with 185 subjects to evaluate the performance of the gait verification with various spatial and temporal resolutions, demonstrate the effectiveness of the proposed method. Keywords-gait recognition; low frame rate; phase synchro-nization; gait period; PCA I

Cross-View Gait Recognition Using View-Dependent Discriminative Analysis

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Annotator-dependent uncertainty-aware estimation of gait relative attributes

In this paper, we describe an uncertainty-aware estimation framework for gait relative attributes. We specifically design a two-stream network model that takes a pair of gait videos as input. It then outputs a corresponding pair of Gaussian distributions of gait absolute attribute scores and annotator-dependent gait relative attribute label distributions. Moreover, we propose a differentiable annotator-independent uncertainty layer to estimate the gait relative attribute score distribution from the absolute distributions then map it to a relative attribute label distribution using the computation of cumulative distribution functions. Furthermore, we propose another annotator-dependent uncertainty layer to estimate the uncertainty on the gait relative attribute labels in terms of a set of trainable transition matrices. Finally, we design a joint loss function on the relative attribute label distribution to learn the model parameters. Experiments on two gait relative attribute datasets demonstrated the effectiveness of the proposed method against baselines in quantitative and qualitative evaluations

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network