Markerless View Independent Gait Analysis with Self-camera Calibration

We present a new method for viewpoint independent markerless gait analysis. The system uses a single camera, does not require camera calibration and works with a wide range of directions of walking. These properties make the proposed method particularly suitable for identification by gait, where the advantages of completely unobtrusiveness, remoteness and covertness of the biometric system preclude the availability of camera information and use of marker based technology. Tests on more than 200 video sequences with subjects walking freely along different walking directions have been performed. The obtained results show that markerless gait analysis can be achieved without any knowledge of internal or external camera parameters and that the obtained data that can be used for gait biometrics purposes. The performance of the proposed method is particularly encouraging for its appliance in surveillance scenarios

Towards automated visual surveillance using gait for identity recognition and tracking across multiple non-intersecting cameras

Despite the fact that personal privacy has become a major concern, surveillance technology is now becoming ubiquitous in modern society. This is mainly due to the increasing number of crimes as well as the essential necessity to provide secure and safer environment. Recent research studies have confirmed now the possibility of recognizing people by the way they walk i.e. gait. The aim of this research study is to investigate the use of gait for people detection as well as identification across different cameras. We present a new approach for people tracking and identification between different non-intersecting un-calibrated stationary cameras based on gait analysis. A vision-based markerless extraction method is being deployed for the derivation of gait kinematics as well as anthropometric measurements in order to produce a gait signature. The novelty of our approach is motivated by the recent research in biometrics and forensic analysis using gait. The experimental results affirmed the robustness of our approach to successfully detect walking people as well as its potency to extract gait features for different camera viewpoints achieving an identity recognition rate of 73.6 % processed for 2270 video sequences. Furthermore, experimental results confirmed the potential of the proposed method for identity tracking in real surveillance systems to recognize walking individuals across different views with an average recognition rate of 92.5 % for cross-camera matching for two different non-overlapping views.<br/

Covariate Analysis for View-point Independent Gait Recognition

Many studies have shown that gait can be deployed as a biometric. Few of these have addressed the effects of view-point and covariate factors on the recognition process. We describe the first analysis which combines view-point invariance for gait recognition which is based on a model-based pose estimation approach from a single un-calibrated camera. A set of experiments are carried out to explore how such factors including clothing, carrying conditions and view-point can affect the identification process using gait. Based on a covariate-based probe dataset of over 270 samples, a recognition rate of 73.4% is achieved using the KNN classifier. This confirms that people identification using dynamic gait features is still perceivable with better recognition rate even under the different covariate factors. As such, this is an important step in translating research from the laboratory to a surveillance environment

Human Perambulation as a Self Calibrating Biometric

This paper introduces a novel method of single camera gait reconstruction which is independent of the walking direction and of the camera parameters. Recognizing people by gait has unique advantages with respect to other biometric techniques: the identification of the walking subject is completely unobtrusive and the identification can be achieved at distance. Recently much research has been conducted into the recognition of frontoparallel gait. The proposed method relies on the very nature of walking to achieve the independence from walking direction. Three major assumptions have been done: human gait is cyclic; the distances between the bone joints are invariant during the execution of the movement; and the articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The method has been tested on several subjects walking freely along six different directions in a small enclosed area. The results show that recognition can be achieved without calibration and without dependence on view direction. The obtained results are particularly encouraging for future system development and for its application in real surveillance scenarios

On Using Gait in Forensic Biometrics

Given the continuing advances in gait biometrics, it appears prudent to investigate the translation of these techniques for forensic use. We address the question as to the confidence that might be given between any two such measurements. We use the locations of ankle, knee and hip to derive a measure of the match between walking subjects in image sequences. The Instantaneous Posture Match algorithm, using Harr templates, kinematics and anthropomorphic knowledge is used to determine their location. This is demonstrated using real CCTV recorded at Gatwick Airport, laboratory images from the multi-view CASIA-B dataset and an example of real scene of crime video. To access the measurement confidence we study the mean intra- and inter-match scores as a function of database size. These measures converge to constant and separate values, indicating that the match measure derived from individual comparisons is considerably smaller than the average match measure from a population

Validation of a single camera, spatio-temporal gait analysis system

such as tennis. However during competition, it is impracticable to instrument players. A markerless, view-independent, footsurface contact identification (FSCi) system was developed and validated. The FSCi system analysed standard colour video sequences of walking and running (barefoot and shod) from four unique camera perspectives; output data were compared to three-dimensional motion analysis. Results demonstrated that data for 99.6% of foot contacts (all camera perspectives) were identified. The calculation of gait variables, i.e. step length etc., was performed automatically for 91.3% of foot contact data; 8.7% of data required manual intervention for analysis. Resultant direction root-mean square error (RMSE) for foot contact position was 52.1 and 52.2 mm for barefoot and shod walking respectively. Resultant direction RMSE for foot contact position during running was 91.4 and 103.4 mm for barefoot and shod conditions respectively. The FSCi system measured basic gait parameters of walking and running without interfering with the activity being observed. The system represents a flexible approach which could be used for in situ gait analysis. The FSCi system could be used for gait analysis in competitive tennis however performance of the system when applied to larger filming areas, e.g. tennis courts, must be evaluated

Using the Microsoft Kinect to assess human bimanual coordination

Optical marker-based systems are the gold-standard for capturing three-dimensional (3D) human kinematics. However, these systems have various drawbacks including time consuming marker placement, soft tissue movement artifact, and are prohibitively expensive and non-portable. The Microsoft Kinect is an inexpensive, portable, depth camera that can be used to capture 3D human movement kinematics. Numerous investigations have assessed the Kinect\u27s ability to capture postural control and gait, but to date, no study has evaluated it\u27s capabilities for measuring spatiotemporal coordination. In order to investigate human coordination and coordination stability with the Kinect, a well-studied bimanual coordination paradigm (Kelso, 1984, Kelso; Scholz, & Schöner, 1986) was adapted. ^ Nineteen participants performed ten trials of coordinated hand movements in either in-phase or anti-phase patterns of coordination to the beat of a metronome which was incrementally sped up and slowed down. Continuous relative phase (CRP) and the standard deviation of CRP were used to assess coordination and coordination stability, respectively.^ Data from the Kinect were compared to a Vicon motion capture system using a mixed-model, repeated measures analysis of variance and intraclass correlation coefficients (2,1) (ICC(2,1)).^ Kinect significantly underestimated CRP for the the anti-phase coordination pattern (p \u3c.0001) and overestimated the in-phase pattern (p\u3c.0001). However, a high ICC value (r=.097) was found between the systems. For the standard deviation of CRP, the Kinect exhibited significantly higher variability than the Vicon (p \u3c .0001) but was able to distinguish significant differences between patterns of coordination with anti-phase variability being higher than in-phase (p \u3c .0001). Additionally, the Kinect was unable to accurately capture the structure of coordination stability for the anti-phase pattern. Finally, agreement was found between systems using the ICC (r=.37).^ In conclusion, the Kinect was unable to accurately capture mean CRP. However, the high ICC between the two systems is promising and the Kinect was able to distinguish between the coordination stability of in-phase and anti-phase coordination. However, the structure of variability as movement speed increased was dissimilar to the Vicon, particularly for the anti-phase pattern. Some aspects of coordination are nicely captured by the Kinect while others are not. Detecting differences between bimanual coordination patterns and the stability of those patterns can be achieved using the Kinect. However, researchers interested in the structure of coordination stability should exercise caution since poor agreement was found between systems

MARKERLESS MOTION CAPTURE WITHIN SPORT: AN EXPLORATORY CASE STUDY

The purpose of this case study was to compare centre of mass (CoM) recorded by a markerless motion capture system (60 Hz) to a criterion marker based system (120 Hz). Gait kinematics of one healthy male participant was recorded five times by both capture systems simultaneously. CoM position was assessed using a full body six degrees of freedom model, normalised to the stance phase based on a 20 N vertical force threshold recorded with force plates. T-tests on RMSE indicated frontal (0.002 m) and sagittal (0.066 m) CoM coordinates were not significantly different between systems, transverse CoM (0.020 m) was significantly different. Statistical parametric mapping showed significant difference in sagittal CoM during the last 20% of stance. Markerless systems show promise in accurately assessing CoM. Future work should focus on sport actions with larger cohorts

Differentiable Biomechanics Unlocks Opportunities for Markerless Motion Capture

Recent developments have created differentiable physics simulators designed for machine learning pipelines that can be accelerated on a GPU. While these can simulate biomechanical models, these opportunities have not been exploited for biomechanics research or markerless motion capture. We show that these simulators can be used to fit inverse kinematics to markerless motion capture data, including scaling the model to fit the anthropomorphic measurements of an individual. This is performed end-to-end with an implicit representation of the movement trajectory, which is propagated through the forward kinematic model to minimize the error from the 3D markers reprojected into the images. The differential optimizer yields other opportunities, such as adding bundle adjustment during trajectory optimization to refine the extrinsic camera parameters or meta-optimization to improve the base model jointly over trajectories from multiple participants. This approach improves the reprojection error from markerless motion capture over prior methods and produces accurate spatial step parameters compared to an instrumented walkway for control and clinical populations

Markerless Human Motion Analysis

Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. Quantitative information about human motion is fundamental to study how our Central Nervous System controls and organizes movements to functionally evaluate motor performance and deficits. In the last decades, the research in this field has made considerable progress. State-of-the-art technologies that provide useful and accurate quantitative measures rely on marker-based systems. Unfortunately, markers are intrusive and their number and location must be determined a priori. Also, marker-based systems require expensive laboratory settings with several infrared cameras. This could modify the naturalness of a subject\u2019s movements and induce discomfort. Last, but not less important, they are computationally expensive in time and space. Recent advances on markerless pose estimation based on computer vision and deep neural networks are opening the possibility of adopting efficient video-based methods for extracting movement information from RGB video data. In this contest, this thesis presents original contributions to the following objectives: (i) the implementation of a video-based markerless pipeline to quantitatively characterize human motion; (ii) the assessment of its accuracy if compared with a gold standard marker-based system; (iii) the application of the pipeline to different domains in order to verify its versatility, with a special focus on the characterization of the motion of preterm infants and on gait analysis. With the proposed approach we highlight that, starting only from RGB videos and leveraging computer vision and machine learning techniques, it is possible to extract reliable information characterizing human motion comparable to that obtained with gold standard marker-based systems