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Sensors 2017, 17(10), 2354; doi:10.3390/s17102354

Nighttime Foreground Pedestrian Detection Based on Three-Dimensional Voxel Surface Model

1
School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
2
School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
*
Authors to whom correspondence should be addressed.
Received: 23 July 2017 / Revised: 4 October 2017 / Accepted: 12 October 2017 / Published: 16 October 2017
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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Abstract

Pedestrian detection is among the most frequently-used preprocessing tasks in many surveillance application fields, from low-level people counting to high-level scene understanding. Even though many approaches perform well in the daytime with sufficient illumination, pedestrian detection at night is still a critical and challenging problem for video surveillance systems. To respond to this need, in this paper, we provide an affordable solution with a near-infrared stereo network camera, as well as a novel three-dimensional foreground pedestrian detection model. Specifically, instead of using an expensive thermal camera, we build a near-infrared stereo vision system with two calibrated network cameras and near-infrared lamps. The core of the system is a novel voxel surface model, which is able to estimate the dynamic changes of three-dimensional geometric information of the surveillance scene and to segment and locate foreground pedestrians in real time. A free update policy for unknown points is designed for model updating, and the extracted shadow of the pedestrian is adopted to remove foreground false alarms. To evaluate the performance of the proposed model, the system is deployed in several nighttime surveillance scenes. Experimental results demonstrate that our method is capable of nighttime pedestrian segmentation and detection in real time under heavy occlusion. In addition, the qualitative and quantitative comparison results show that our work outperforms classical background subtraction approaches and a recent RGB-D method, as well as achieving comparable performance with the state-of-the-art deep learning pedestrian detection method even with a much lower hardware cost. View Full-Text
Keywords: nighttime foreground pedestrian detection; voxel surface model; near-infrared stereo network camera nighttime foreground pedestrian detection; voxel surface model; near-infrared stereo network camera
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, J.; Zhang, F.; Wei, L.; Yang, T.; Lu, Z. Nighttime Foreground Pedestrian Detection Based on Three-Dimensional Voxel Surface Model. Sensors 2017, 17, 2354.

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