Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions.
This paper proposes and presents one way for people recognition from video streams. People recognition can be realized using various biometric features, behavioral or physiological, and methods based on that features. This work proposes and describes an algorithm for people recognition from video streams that is composed of two modules, module for dataset creation and module for recognition. Module for dataset creation involves creation of various types of person images and parameters. Module for recognition includes multiple comparisons of the images and different parameters comparison. These parameters are average height and average step length of a person during a gait cycle. For experimental purposes, a dataset for 15 persons in gait is created using a long-range stereo camera in outdoor environment. The algorithm has high accuracy in people recognition and easily can be upgraded with additional steps and modules, so it is suitable for use in various applications.
Each person describes unique patterns during gait cycles and this information can be extracted from live video stream and used for subject identification. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. In this paper, a method to enhance the appearance-based gait recognition method by also integrating features extracted from depth data is proposed. Two approaches are proposed that integrate simple depth features in a way suitable for real-time processing. Unlike previously presented works which usually use a short range sensors like Microsoft Kinect, here, a long-range stereo camera in outdoor environment is used. The experimental results for the proposed approaches show that recognition rates are improved when compared to existing popular gait recognition methods.
In this paper, we present an example of solution for obtaining some key elements required for people identification or re-identification. During an identification or re-identification process, different elements are in use. Depending on the method to be implemented, said elements can be RGB (red, green, blue) images, grayscale images, different types of features extracted from them, etc. In this work, we focused on obtaining certain elements suitable for use in the gait recognition methods or re-identification methods that use the obtained features from the images with people in gait. The presented solution can be useful in many applications of identification or re-identification, since key elements required by the implemented methods can be obtained in a simple way. Also, different methods for identification or re-identification can be added in the presented solution. Based on this, we have developed a simple system for people re-identification. An experiment was conducted on our own dataset containing 13 people in gait. The results obtained were over 90% for 4 out of 5 types of features used in the presented re-identification system. It is important to emphasize that more reliable results can be obtained by combining different types of features.
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