Gait recognition using normalized shadows

WOS:000426986000189 (Nº de Acesso Web of Science)Surveillance of public spaces is often conducted with the help of cameras placed at elevated positions. Recently, drones with high resolution cameras have made it possible to perform overhead surveillance of critical spaces. However, images obtained in these conditions may not contain enough body features to allow conventional biometric recognition. This paper introduces a novel gait recognition system which uses the shadows cast by users, when available. It includes two main contributions: (i) a method for shadow segmentation, which analyzes the orientation of the silhouette contour to identify the feet position along time, in order to separate the body and shadow silhouettes connected at such positions; (ii) a method that normalizes the segmented shadow silhouettes, by applying a transformation derived from optimizing the low rank textures of a gait texture image, to compensate for changes in view and shadow orientation. The normalized shadow silhouettes can then undergo a gait recognition algorithm, which in this paper relies on the computation of a gait energy image, combined with linear discriminant analysis for user recognition. The proposed system outperforms the available state-of-the-art, being robust to changes in acquisition viewpoints.info:eu-repo/semantics/acceptedVersio

A spatiotemporal deep learning approach for automatic pathological Gait classification

Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.info:eu-repo/semantics/publishedVersio

Remote Gait type classification system using markerless 2D video

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating 5 types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating 5 types of gait, at 2 severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.info:eu-repo/semantics/publishedVersio