Identification of people by analysis of gait patterns extracted from video has recently become a popular research problem. However, the conditions under which the problem is "solvable" are not understood or characterized. To provide a means for measuring progress and characterizing the properties of gait recognition, we introduce the humanlD gait challenge problem. The challenge problem consists of a baseline algorithm, a set of 12 experiments, and a large data set. The baseline algorithm estimates silhouettes by background subtraction and performs recognition by temporal correlation of silhouettes. The 12 experiments are of increasing difficulty, as measured by the baseline algorithm, and examine the effects of five covariates on performance. The covariates are: change in viewing angle, change in shoe type, change in walking surface, carrying or not carrying a briefcase, and elapsed time between sequences being compared. Identification rates for the 12 experiments range from 78 percent on the easiest experiment to 3 percent on the hardest. All five covariates had statistically significant effects on performance, with walking surface and time difference having the greatest impact. The data set consists of 1,870 sequences from 122 subjects spanning five covariates (1.2 gigabytes of data). This infrastructure supports further development of gait recognition algorithms and additional experiments to understand the strengths and weaknesses of new algorithms. The experimental results are presented, the more detailed is the possible meta-analysis and greater is the understanding. It is this potential from the adoption of this challenge problem that represents a radical departure from traditional computer vision research methodology.

Recognition of people through gait analysis is an important research topic, with potential applications in video surveillance, tracking, and monitoring. Recognizing the importance of evaluating and comparing possible competing solutions to this problem, we previously introduced the HumanID challenge problem consisting of a set of experiments of increasing difficulty, a baseline algorithm, and a large set of video sequences (about 300 GB of data related to 452 sequences from 74 subjects) acquired to investigate important dimensions to this problem, such as variations due to viewpoint, footwear and walking surface. In this paper we present a detailed investigation of the baseline algorithm, quantify the dependence of the various covariates on gait-based identification, and update the previous baseline performance with optimized ones. We establish that the performance of the baseline algorithm is robust with respect to its various parameters. The overall identification performance is also stable with respect to the quality of the silhouettes. We find that the approximately lower 20% of the silhouette accounts for most of the recognition achieved. Viewpoint has barely statistically significant effect on identification rates, whereas footwear and surface-type does have significant effects with the effect due to surface-type being approximately 5 times that of shoe-type.

Identification of people from gait captured on video has become a challenge problem in computer vision. However there is not a baseline algorithm or standard dataset for measuring, or determining what factors affect performance. In fact, the conditions under which the problem is "solvable" are not understood or characterized. This paper describes a large set of video sequences (about 300 GB of data related to 452 sequences from 74 subjects) acquired to investigate important dimensions of this problem, such as variations due to viewpoint, footwear, and walking surface. We introduce the HumanID challenge problem. The challenge problem contains a set of experiments of increasing difficulty, a baseline algorithm, and its performance on the challenge problem. Our results suggest that differences in footwear or walking surface type between the gallery and probe video sequence are factors that affect performance. The data set, the source code for the baseline algorithm, and UNIX scripts to reproduce the basic results reported are available to the research community at http://marathon.csee.usf.edu/GaitBaseline/.

High confidence in disparity map estimation is critical in several application fields. A novel framework that employs customized local binary patterns and Jaccard distance for stereo matching along stereo consistency checks is presented. The proposal contributes with a method that allows greater confidence in its estimates, without dependence on supervised learning, and capable of generating a dense map with low-cost filtering. The proposed framework has been implemented in CPU and GPU for parallel processing capability. First, Local binary patterns are obtained during the initial stage; then, the Jaccard distance is employed as a similarity measure in the stereo matching stage; subsequently, a matching consistency check is performed, and singular disparities are removed. A comparison among novel and state-of-the-art algorithms for sparse disparity map estimation is performed employing Middlebury and KITTI stereo Datasets where the quality criteria used were percentage of bad pixels (B), quantity of invalid pixels, processing time and running environments to put each framework into context, obtaining down to 2.07% bad matching pixels and performing better than state-of-the-art cost functions

In this article, the theory of fractional control and state estimation applied to biological science is studied, particularly in hybrid wastewater treatment. For nonlinear systems with stable and known states, an interconnected fractional robust control design with high gain state estimation is proposed to generate a control insensitive to nutritional perturbations originated by an advanced oxidation process in a microalgae culture. An online study is proposed for the mineralization of glyphosate and its feedback in a microalgae cultivation process where through the designed control the light dynamics is manipulated to robustly and automatically regulate the biomass signal provided by an analog sensor and nutrient estimation via state observers. In the literature, there are few results developed with real-time results. This work is a multidisciplinary study with online results where the performance and improvement of the proposed complex process are concluded.

We consider the use of nonstationarity in the distribution of feature relationships over time for walking gait-based recognition. We statistically model the features of a person by computing the distribution of the relations among the features, rather than the features themselves. These relational distributions of feature relations are represented as points in a space of probability functions. Our database presently consists of twenty subjects walking outdoors along three different paths at 0/spl deg/ (frontal-parallel), 22/spl deg/ and 45/spl deg/ with respect to the image plane and walking in both directions, left to right and right to left. We performed statistical tests to demonstrate that variations between persons are statistically more significant than the variations due to walking angles and walking directions. We also present identification results on people walking at different directions and different angles.