A vehicle detection and shadow elimination method based on greyscale information, edge information, and prior knowledge

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).

Automated summary

The paper proposes a robust vehicle detection method with shadow elimination, which outperforms the faster R-CNN and SSD methods in terms of real-time performance and accuracy through two-step processing with multiple information inputs. This method has broad application prospects in ITS.