A data augmentation methodology for training machine/deep learning gait recognition algorithms

There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait; they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible

Fast human activity recognition based on structure and motion

This is the post-print version of the final paper published in Pattern Recognition Letters. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2011 Elsevier B.V.We present a method for the recognition of human activities. The proposed approach is based on the construction of a set of templates for each activity as well as on the measurement of the motion in each activity. Templates are designed so that they capture the structural and motion information that is most discriminative among activities. The direct motion measurements capture the amount of translational motion in each activity. The two features are fused at the recognition stage. Recognition is achieved in two steps by calculating the similarity between the templates and motion features of the test and reference activities. The proposed methodology is experimentally assessed and is shown to yield excellent performance.European Commissio

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness

Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient

Gait recognition using the energy image representation of the average silhouette image in one complete cycle becomes a baseline in model-free approaches research. Nevertheless, gait is sensitive to any changes. Up to date in the area of feature extraction, image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat. Although the use of Histogram of orientation Gradient (HOG) in pedestrian detection is the most effective method, its accuracy is still considered low after testing on covariate dataset. Thus this research proposed a combination of frequency and spatial features based on Inverse Fast Fourier Transform and Histogram of Oriented Gradient (IFFTG-HoG) for gait recognition. It consists of three phases, namely image processing phase, feature extraction phase in the production of a new image representation and the classification. The first phase comprises image binarization process and energy image generation using gait average image in one cycle. In the second phase, the IFFTG-HoG method is used as a features gait extraction after generating energy image. Here, the IFFTG-HoG method has also been improved by using Chebyshev distance to calculate the magnitude of the gradient to increase the rate of recognition accuracy. Lastly, K-Nearest Neighbour (k=NN) classifier with K=1 is employed for individual classification in the third phase. A total of 124 people from CASIA B dataset were tested using the proposed IFTG-HoG method. It performed better in gait individual classification as the value of average accuracy for the standard dataset 96.7%, 93.1% and 99.6%compared to HoG method by 94.1%, 85.9% and 96.2% in order. With similar motivation, we tested on Rempit datasets to recognize motorcycle rider anomaly event and our proposed method also outperforms Dalal Method