Reliable moving vehicle detection based on the filtering of swinging tree leaves and raindrops

An efficient method for detecting moving vehicles based on the filtering of swinging trees and raindrops is proposed. To extract moving objects from the background, an adaptive background subtraction scheme with a shadow elimination model is used. Swinging trees are removed from foreground objects to reduce the computational complexity of subsequent tracking. Raindrops are removed from foreground objects when necessary. Performance evaluations are carried out using seven real-world traffic image sequences. Experimental results show average recognition rates of 96.83% and 97.20% for swinging trees and raindrops, respectively, indicating the feasibility of the proposed method.

► Swinging trees and raindrops filtering greatly improves to detect moving vehicles.
► Swinging trees filtering is achieved using motion estimation and compensation.
► Three salient features are used to reliably remove raindrops from the background.
► Adaptive background modeling is used to extract moving objects from the background.
► Over 95 of the detection rates of swinging trees and raindrops can be achieved.