Nighttime Foreground Pedestrian Detection Based on Three-Dimensional Voxel Surface Model
| Autor: | Fangbing Zhang, Tao Yang, Zhaoyang Lu, Lisong Wei, Jing Li |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
Pedestrian detection ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Pedestrian lcsh:Chemical technology Biochemistry Article Analytical Chemistry 0502 economics and business Shadow 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Segmentation Computer vision Electrical and Electronic Engineering voxel surface model Instrumentation ComputingMethodologies_COMPUTERGRAPHICS 050210 logistics & transportation Background subtraction business.industry Deep learning 05 social sciences Atomic and Molecular Physics and Optics Stereopsis near-infrared stereo network camera RGB color model 020201 artificial intelligence & image processing Artificial intelligence nighttime foreground pedestrian detection business |
| Zdroj: | Sensors, Vol 17, Iss 10, p 2354 (2017) Sensors (Basel, Switzerland) Sensors; Volume 17; Issue 10; Pages: 2354 |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s17102354 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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