A vehicle detection and shadow elimination method based on greyscale information, edge information, and prior knowledge. (September 2021)

• Record Type: Journal Article
• Authors: Zhang, Jing; Guo, Xinchang; Zhang, Chufeng; Liu, Peng
• Is Part Of: Computers & electrical engineering. Volume 94(2021)
• Publication Date: 2021-09
• Subjects: Vehicle detection -- Shadow elimination -- Edge detection -- Object segmentation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107366
• Languages: English
• ISSNs: 0045-7906

Abstract

Hightlights: The shadow problem harms the accuracy of vehicle detection. Detecting vehicles using edge and grayscale information can simplify the algorithm. The shadow location information can help improving the accuracy of shadow detection. Abstract: Vehicle detection is one of the most fundamental aspects of traffic surveillance systems. However, the shadow problem often hinders the accuracy of vehicle detection. Shadows are often mistakenly understood as the parts of a vehicle, causing objects loss, or shape distortion. Collected data with these errors are unreliable, therefore shadow detection and elimination is a key step to promote the accuracy of vehicle detection. This paper proposes a robust vehicle detection method with shadow elimination. The method is divided into two steps: Firstly, we extract foreground regions using a background differential method based on edge information, then we detect and eliminate the shadows from the foreground regions combined with grayscale information, edge information, and prior knowledge. The experimental results show that the proposed method is superior to the faster R-CNN, and SSD methods in terms of real-time performance and vehicle detection accuracy. The proposed method has broad application prospects in the Intelligent Transportation Systems (ITS). Graphical abstract: Image, graphical abstract