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

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

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

Crossmodal Point Cloud Registration in the Hough Space for Mobile Laser Scanning Data

Sign.: [ ], A-Z, 2A-2Z, 3A-3M, 3NAs f. preg. de lam., son grav. cal