Authors:
Zhongchuan Zhang
and
Fernand Cohen
Affiliation:
Drexel University, United States
Keyword(s):
Pedestrian Tracking, Overhead Camera, Head Detection, 3d Position Estimation, Facial Image Capture.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Tracking and Visual Navigation
;
Video Surveillance and Event Detection
Abstract:
In this paper, we introduce a 3D pedestrian tracking method based on 3D head point detection in indoor environment, such as train stations, airports, shopping malls and hotel lobbies where the ground can be non-flat. We also show that our approach is effective and efficient in capturing close-up facial images using pan-tilt-zoom (PTZ) cameras. We use two horizontally displaced overhead cameras to track pedestrians by estimating the accurate 3D position of their heads. The 3D head point is then tracked using common assumptions on motion direction and velocity. Our method is able to track pedestrians in 3D space no matter if the pedestrian is walking on a planar or a non-planar surface. Moreover, we make no assumption about the pedestrians’ heights, nor do we have to generate the full disparity map of the scene. The tracking system architecture allows for a real time capturing of high quality facial images by guiding PTZ cameras. The approach is tested using a publicly available visual
surveillance simulation test bed.
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