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.
Highlights
Pedestrian detection is an important topic and can be applied to a variety of computer vision applications [1], such as intelligent video surveillance [2], autonomous service robots, human–computer interaction, self-driving cars and ADAS (Advanced Driver Assistant System) [3]
We present a novel nighttime pedestrian detection system based on the voxel surface model to detect pedestrians at night, which can solve the problem of partial occlusion and be used in a real-time system
We know this system can obtain a variety of output results, including foreground binary image, foreground cluster depth and human detection results, and the results of the detection confirm that our system can be applied to actual life and has a robust detection performance
Summary
Pedestrian detection is an important topic and can be applied to a variety of computer vision applications [1], such as intelligent video surveillance [2], autonomous service robots, human–computer interaction, self-driving cars and ADAS (Advanced Driver Assistant System) [3]. From the beginning of the development of computer vision to the present, there have been many pedestrian detection algorithms that have been proposed and with continuous improvement. Most of these studies are carried out during the day with good illumination, and many daytime pedestrian detection systems cannot be used directly to detect pedestrians at night [4]. We select the appropriate clustering window width w according to the size of the pedestrian. In this way, we can quickly locate all of the foreground pedestrians.
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