We proposed in [1] an integrated object detection and classification pipeline using deep learning techniques for extracting various object categories in continuously streamed LIDAR point clouds collected from urban areas.
A public benchmark database called SZTAKI-Velo64Road is also released for evaluating object recognition algorithms in urban environments based on real time Lidar measurements of a Velodyne HDL 64-E sensor.
3D WebGL demo on object detection and classifaction
Object detection and recognition in a street scenario with four object classes: pedestrian, street clutter, vehicle and short facade .
WebGL demo's authors: A. Börcs, B. Nagy, G. Sepovics and C. Benedek
Reference
[1] A. Börcs, B. Nagy and Cs. Benedek: "Instant Object Detection in Lidar Point Clouds", IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 7, pp. 992 - 996, 2017, IF: 2.892 note: cover page article
Geo-Information Computing @ Machine Perception Lab.
GeoComp Demos:
GeoComp Group leader: Dr. Csaba Benedek benedek.csaba@sztaki.hu
i4D project manager: Dr. Zsolt Jankó janko.zsolt@sztaki.hu
Head of MPLab: Prof. Tamás Szirányi
MPLab administration: Anikó Vágvölgyi
Address:
SZTAKI