Recurrent Attention Models for Depth-Based Person Identification

We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201

2.5D multi-view gait recognition based on point cloud registration

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM

Gait Recognition

Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced

Using Skeleton Correction to Improve Flash Lidar-Based Gait Recognition

This paper presents GlidarPoly, an efficacious pipeline of 3D gait recognition for flash lidar data based on pose estimation and robust correction of erroneous and missing joint measurements. A flash lidar can provide new opportunities for gait recognition through a fast acquisition of depth and intensity data over an extended range of distance. However, the flash lidar data are plagued by artifacts, outliers, noise, and sometimes missing measurements, which negatively affects the performance of existing analytics solutions. We present a filtering mechanism that corrects noisy and missing skeleton joint measurements to improve gait recognition. Furthermore, robust statistics are integrated with conventional feature moments to encode the dynamics of the motion. As a comparison, length-based and vector-based features extracted from the noisy skeletons are investigated for outlier removal. Experimental results illustrate the superiority of the proposed methodology in improving gait recognition given noisy, low-resolution flash lidar data

Robust arbitrary-view gait recognition based on 3D partial similarity matching

Existing view-invariant gait recognition methods encounter difficulties due to limited number of available gait views and varying conditions during training. This paper proposes gait partial similarity matching that assumes a 3-dimensional (3D) object shares common view surfaces in significantly different views. Detecting such surfaces aids the extraction of gait features from multiple views. 3D parametric body models are morphed by pose and shape deformation from a template model using 2-dimensional (2D) gait silhouette as observation. The gait pose is estimated by a level set energy cost function from silhouettes including incomplete ones. Body shape deformation is achieved via Laplacian deformation energy function associated with inpainting gait silhouettes. Partial gait silhouettes in different views are extracted by gait partial region of interest elements selection and re-projected onto 2D space to construct partial gait energy images. A synthetic database with destination views and multi-linear subspace classifier fused with majority voting are used to achieve arbitrary view gait recognition that is robust to varying conditions. Experimental results on CMU, CASIA B, TUM-IITKGP, AVAMVG and KY4D datasets show the efficacy of the propose method

Estimation and validation of temporal gait features using a markerless 2D video system

Background and Objective: Estimation of temporal gait features, such as stance time, swing time and gait cycle time, can be used for clinical evaluations of various patient groups having gait pathologies, such as Parkinson’s diseases, neuropathy, hemiplegia and diplegia. Most clinical laboratories employ an optoelectronic motion capture system to acquire such features. However, the operation of these systems requires specially trained operators, a controlled environment and attaching reflective markers to the patient’s body. To allow the estimation of the same features in a daily life setting, this paper presents a novel vision based system whose operation does not require the presence of skilled technicians or markers and uses a single 2D camera. Method: The proposed system takes as input a 2D video, computes the silhouettes of the walking person, and then estimates key biomedical gait indicators, such as the initial foot contact with the ground and the toe off instants, from which several other temporal gait features can be derived. Results: The proposed system is tested on two datasets: (i) a public gait dataset made available by CASIA, which contains 20 users, with 4 sequences per user; and (ii) a dataset acquired simultaneously by a marker-based optoelectronic motion capture system and a simple 2D video camera, containing 10 users, with 5 sequences per user. For the CASIA gait dataset A the relevant temporal biomedical gait indicators were manually annotated, and the proposed automated video analysis system achieved an accuracy of 99% on their identification. It was able to obtain accurate estimations even on segmented silhouettes where, the state-of-the-art markerless 2D video based systems fail. For the second database, the temporal features obtained by the proposed system achieved an average intra-class correlation coefficient of 0.86, when compared to the "gold standard" optoelectronic motion capture system. Conclusions: The proposed markerless 2D video based system can be used to evaluate patients’ gait without requiring the usage of complex laboratory settings and without the need for physical attachment of sensors/markers to the patients. The good accuracy of the results obtained suggests that the proposed system can be used as an alternative to the optoelectronic motion capture system in non-laboratory environments, which can be enable more regular clinical evaluations.info:eu-repo/semantics/acceptedVersio