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

Lidar-based Gait Analysis and Activity Recognition in a 4D Surveillance System

This paper presents new approaches for gait and activity analysis based on data streams of a Rotating Multi Beam (RMB) Lidar sensor. The proposed algorithms are embedded into an integrated 4D vision and visualization system, which is able to analyze and interactively display real scenarios in natural outdoor environments with walking pedestrians. The main focus of the investigations are gait based person re-identification during tracking, and recognition of specific activity patterns such as bending, waving, making phone calls and checking the time looking at wristwatches. The descriptors for training and recognition are observed and extracted from realistic outdoor surveillance scenarios, where multiple pedestrians are walking in the field of interest following possibly intersecting trajectories, thus the observations might often be affected by occlusions or background noise. Since there is no public database available for such scenarios, we created and published a new Lidar-based outdoors gait and activity dataset on our website, that contains point cloud sequences of 28 different persons extracted and aggregated from 35 minutes-long measurements. The presented results confirm that both efficient gait-based identification and activity recognition is achievable in the sparse point clouds of a single RMB Lidar sensor. After extracting the people trajectories, we synthesized a free-viewpoint video, where moving avatar models follow the trajectories of the observed pedestrians in real time, ensuring that the leg movements of the animated avatars are synchronized with the real gait cycles observed in the Lidar stream

Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis

This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multiview gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Cross-domain self-supervised complete geometric representation learning for real-scanned point cloud based pathological gait analysis

Accurate lower-limb pose estimation is a prereq-uisite of skeleton based pathological gait analysis. To achievethis goal in free-living environments for long-term monitoring,single depth sensor has been proposed in research. However,the depth map acquired from a single viewpoint encodes onlypartial geometric information of the lower limbs and exhibitslarge variations across different viewpoints. Existing off-the-shelfthree-dimensional (3D) pose tracking algorithms and publicdatasets for depth based human pose estimation are mainlytargeted at activity recognition applications. They are relativelyinsensitive to skeleton estimation accuracy, especially at thefoot segments. Furthermore, acquiring ground truth skeletondata for detailed biomechanics analysis also requires consid-erable efforts. To address these issues, we propose a novelcross-domain self-supervised complete geometric representationlearning framework, with knowledge transfer from the unlabelledsynthetic point clouds of full lower-limb surfaces. The proposedmethod can significantly reduce the number of ground truthskeletons (with only 1%) in the training phase, meanwhileensuring accurate and precise pose estimation and capturingdiscriminative features across different pathological gait patternscompared to other methods

Feature selection for Lidar-based gait recognition

In this paper, we present a performance analysis of various descriptors suited to human gait analysis in Rotating Multi-Beam (RMB) Lidar measurement sequences. The gait descriptors for training and recognition are observed and extracted in realistic outdoor surveillance scenarios, where multiple pedestrians walk concurrently in the field of interest, their trajectories often intersect, while occlusions or background noise may affects the observation. For the Lidar scenes, we compared the modifications of five approaches proposed originally for optical cameras or Kinect measurements. Our results confirmed that efficient person re-identification can be achieved using a single Lidar sensor, even if it produces sparse point clouds

4D Feet: Registering Walking Foot Shapes Using Attention Enhanced Dynamic-Synchronized Graph Convolutional LSTM Network

4D scans of dynamic deformable human body parts help researchers have a better understanding of spatiotemporal features. However, reconstructing 4D scans based on multiple asynchronous cameras encounters two main challenges: 1) finding the dynamic correspondences among different frames captured by each camera at the timestamps of the camera in terms of dynamic feature recognition, and 2) reconstructing 3D shapes from the combined point clouds captured by different cameras at asynchronous timestamps in terms of multi-view fusion. In this paper, we introduce a generic framework that is able to 1) find and align dynamic features in the 3D scans captured by each camera using the nonrigid iterative closest-farthest points algorithm; 2) synchronize scans captured by asynchronous cameras through a novel ADGC-LSTM-based network, which is capable of aligning 3D scans captured by different cameras to the timeline of a specific camera; and 3) register a high-quality template to synchronized scans at each timestamp to form a high-quality 3D mesh model using a non-rigid registration method. With a newly developed 4D foot scanner, we validate the framework and create the first open-access data-set, namely the 4D feet. It includes 4D shapes (15 fps) of the right and left feet of 58 participants (116 feet in total, including 5147 3D frames), covering significant phases of the gait cycle. The results demonstrate the effectiveness of the proposed framework, especially in synchronizing asynchronous 4D scans using the proposed ADGC-LSTM network

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

Intelligent surveillance of indoor environments based on computer vision and 3D point cloud fusion

A real-time detection algorithm for intelligent surveillance is presented. The system, based on 3D change detection with respect to a complex scene model, allows intruder monitoring and detection of added and missing objects, under different illumination conditions. The proposed system has two independent stages. First, a mapping application provides an accurate 3D wide model of the scene, using a view registration approach. This registration is based on computer vision and 3D point cloud. Fusion of visual features with 3D descriptors is used in order to identify corresponding points in two consecutive views. The matching of these two views is first estimated by a pre-alignment stage, based on the tilt movement of the sensor, later they are accurately aligned by an Iterative Closest Point variant (Levenberg-Marquardt ICP), which performance has been improved by a previous filter based on geometrical assumptions. The second stage provides accurate intruder and object detection by means of a 3D change detection approach, based on Octree volumetric representation, followed by a clusters analysis. The whole scene is continuously scanned, and every captured is compared with the corresponding part of the wide model thanks to the previous analysis of the sensor movement parameters. With this purpose a tilt-axis calibration method has been developed. Tests performed show the reliable performance of the system under real conditions and the improvements provided by each stage independently. Moreover, the main goal of this application has been enhanced, for reliable intruder detection by the tilting of the sensors using its built-in motor to increase the size of the monitored area. (C) 2015 Elsevier Ltd. All rights reserved.This work was supported by the Spanish Government through the CICYT projects (TRA2013-48314-C3-1-R) and (TRA2011-29454-C03-02)