Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos

Sensors

卷:18

Xuezhi Xiang¹  Mingliang Zhai¹  Ning Lv¹  Abdulmotaleb El Saddik² 

[1] The School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;

[2] The school of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada;

关键词: vehicle counting;    unmanned aerial vehicle;    vehicle detection;    visual tracking;    aerial video;   

DOI  :  10.3390/s18082560

来源: DOAJ

【 摘 要 】

Vehicle counting from an unmanned aerial vehicle (UAV) is becoming a popular research topic in traffic monitoring. Camera mounted on UAV can be regarded as a visual sensor for collecting aerial videos. Compared with traditional sensors, the UAV can be flexibly deployed to the areas that need to be monitored and can provide a larger perspective. In this paper, a novel framework for vehicle counting based on aerial videos is proposed. In our framework, the moving-object detector can handle the following two situations: static background and moving background. For static background, a pixel-level video foreground detector is given to detect vehicles, which can update background model continuously. For moving background, image-registration is employed to estimate the camera motion, which allows the vehicles to be detected in a reference coordinate system. In addition, to overcome the change of scale and shape of vehicle in images, we employ an online-learning tracker which can update the samples used for training. Finally, we design a multi-object management module which can efficiently analyze and validate the status of the tracked vehicles with multi-threading technique. Our method was tested on aerial videos of real highway scenes that contain fixed-background and moving-background. The experimental results show that the proposed method can achieve more than 90% and 85% accuracy of vehicle counting in fixed-background videos and moving-background videos respectively.

【 授权许可】

Unknown