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

Gait Recognition with Compact Lidar Sensors

In this paper, we present a comparative study on gait and activity analysis using LiDAR scanners with different resolution. Previous studies showed that gait recognition methods based on the point clouds of a Velodyne HDL-64E Rotating Multi-Beam LiDAR can be used for people re-identification in outdoor surveillance scenarios. However, the high cost and the weight of that sensor means a bottleneck for its wide application in surveillance systems. The contribution of this paper is to show that the proposed Lidar-based Gait Energy Image descriptor can be efficiently adopted to the measurements of the compact and significantly cheaper Velodyne VLP-16 LiDAR scanner, which produces point clouds with a nearly four times lower vertical resolution than HDL-64. On the other hand, due to the sparsity of the data, the VLP-16 sensor proves to be less efficient for the purpose of activity recognition, if the events are mainly characterized by fine hand movements. The evaluation is performed on five tests scenarios with multiple walking pedestrians, which have been recorded by both sensors in parallel

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

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

4D Scene Reconstruction in Multi-Target Scenarios

In this report, we introduce a complex approach on 4D reconstruction of dynamic scenarios containing multiple walking pedestrians. The input of the process is a point cloud sequence recorded by a rotating multi-beam Lidar sensor, which monitors the scene from a fixed position. The output is a geometrically reconstructed and textured scene containing moving 4D people models, which can follow in real time the trajectories of the walking pedestrians observed on the Lidar data flow. Our implemented system consists of four main steps. First, we separate foreground and background regions in each point cloud frame of the sequence by a robust probabilistic approach. Second, we perform moving pedestrian detection and tracking, so that among the point cloud regions classified as foreground, we separate the different objects, and assign the corresponding people positions to each other over the consecutive frames of the Lidar measurement sequence. Third, we geometrically reconstruct the ground, walls and further objects of the background scene, and texture the obtained models with photos taken from the scene. Fourth we insert into the scene textured 4D models of moving pedestrians which were preliminary created in a special 4D reconstruction studio. Finally, we integrate the system elements in a joint dynamic scene model and visualize the 4D scenario

LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors

LiDAR can capture accurate depth information in large-scale scenarios without the effect of light conditions, and the captured point cloud contains gait-related 3D geometric properties and dynamic motion characteristics. We make the first attempt to leverage LiDAR to remedy the limitation of view-dependent and light-sensitive camera for more robust and accurate gait recognition. In this paper, we propose a LiDAR-camera-based gait recognition method with an effective multi-modal feature fusion strategy, which fully exploits advantages of both point clouds and images. In particular, we propose a new in-the-wild gait dataset, LiCamGait, involving multi-modal visual data and diverse 2D/3D representations. Our method achieves state-of-the-art performance on the new dataset. Code and dataset will be released when this paper is published