Agnieszka Michalczuk

3D Gait Recognition Using Spatio-Temporal Motion Descriptors

We present a view independent algorithm for 3D human gait recognition. The identification of the person is achieved using motion data obtained by our markerless 3D motion tracking algorithm. We report its tracking accuracy using ground-truth data obtained by a marker-based motion capture system. The classification is done using SVM built on the proposed spatio-temporal motion descriptors. The identification performance was determined using 230 gait cycles performed by 22 persons. The correctly classified ratio achieved by SVM is equal to 93.5% for rank 1 and 99.6% for rank 3. We show that the recognition performance obtained with the spatio-temporal gait signatures is better in comparison to accuracy obtained with tensorial gait data and reduced by MPCA.

View Independent Human Gait Recognition Using Markerless 3D Human Motion Capture

We present an algorithm for view-independent human gait recognition. The human gait recognition is achieved using data obtained by our markerless 3D motion tracking algorithm. The tensorial gait data were reduced by multilinear principal component analysis and subsequently classified. The performance of the motion tracking algorithm was evaluated using ground-truth data from MoCap. The classification accuracy was determined using video sequences with walking performers. Experiments on multiview video sequences show the promising effectiveness of the proposed algorithm.

The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems

This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. The achieved results are compared with analogous outcomes produced by other optimization methods reported in the literature.

Gait recognition based on marker-less 3D motion capture

We present an algorithm for view-independent gait-based person identification. The identification is achieved using data obtained by our marker-less 3D motion tracking algorithm. The motion tracking was accomplished by a particle swarm optimization algorithm. The accuracy of the motion tracking algorithm was evaluated using ground-truth data from MoCap. It was determined on 88 sequences with 22 walking performers. We obtained 90% identification accuracy (rank 1) on 230 gait cycles.

Feature Selection of Motion Capture Data in Gait Identification Challenge Problem

The method of discovering robust gait signatures containing strong discriminative properties is proposed. It is based on feature extraction and selection of motion capture data. Three different approaches of feature extraction applied to Euler angles and their first and second derivates are considered. The proper supervised classification is preceded by specified selection scenario. On the basis of the obtained precision of person gait identification, analyzed feature sets are assessed. To examine proposed method database containing 353 gaits of 25 different males is used. The results are satisfactory. In the best case the recognition accuracy of 97% is achieved. On the basis of classification which takes into consideration only the data of the specified segments, the ranking is constructed. It corresponds to the evaluation of individual features of the joint movements.

Quantifying Chaotic Behavior in Treadmill Walking

The authors describe an example of application of nonlinear time series analysis directed at identifying the presence of deterministic chaos in human motion data by means of the largest Lyapunov exponent (LLE). The research aimed at determination of the influence of gait speed on the LLE value with a view to verification of the belief that slower walking leads to increased stability characterized by smaller LLE value. Analyses were focused on the time series representing hip flexion/extension angle, knee flexion/extension angle and dorsiflexion/plantarflexion dimension of the ankle. Gait sequences were recorded in the Human Motion Laboratory (HML) of the Polish-Japanese Academy of Information Technology in Bytom by means of the Vicon system. Application of the AC5000M treadmill allowed recordings in three variants: at the preferred walking speed (PWS) of each subject, at 80% of the PWS and at 120% of the PWS. According to the recommendations from the literature the LLE value was estimated twice for every time series: as the short-term LLE\(_1\) for the first stride and as the long-term LLE\(_{4-10}\) over a fixed interval between the fourth and the tenth stride. In the latter case it was confirmed that the LLE value increases with walking speed for both limbs.

Feature Extraction and HMM-Based Classification of Gait Video Sequences for the Purpose of Human Identification

The authors present results of the research on human recognition based on the video gait sequences from the CASIA Gait Database. Both linear (principal component analysis; PCA) and non-linear (isometric features mapping; Isomap and locally linear embedding; LLE) methods were applied in order to reduce data dimensionality, whereas a concept of hidden Markov model (HMM) was used for the purpose of data classification. The results of the conducted experiments formed the main subject of analysis of classification accuracy expressed by means of the Correct Classification Rate (CCR).

Optical Flow Based Face Anonymization in Video Sequences.

In this paper we present a method of anonymization of people’s faces in video. Results are analyzed on the basis of optical flow methods. Anonymization bases on face detection. Because of mistakes made by such detectors in video sequences, gaps and false detections appear. They are recognized using the results of face detections and optical flow analysis. In this paper we describe: face detectors and the results of method of analysis of optical-flow based detector. We present novel method of filing gaps and false detection recognizing with use of optical flow. Then we present visual results.

Evaluation of human dynamic balance in Grassmann manifold

The authors present an application of Grassmann manifold to the evaluation of human dynamic balance based on the time series representing movements of hip, knee and ankle joints in the sagittal, frontal and transverse planes. Time series were extracted from gait sequences which were recorded in the Human Motion Laboratory (HML) of the Polish-Japanese Academy of Information Technology in Bytom, Poland using the Vicon system.

Precision of Gait Indices Approximation by Kinect Based Motion Acquisition

Step length constitutes one of the important gait indices. The research described in the present paper was focused on the method of determination of the step length by means of the Kinect device. Gait sequences recorded in the Human Motion Laboratory of the Polish-Japanese Academy of Information Technology using the Vicon system played the role of the reference data. The group of six subjects participated in the experiments. Conclusions from the comparative analysis of the results of both approaches summarize the paper.