Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Abstract

Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.

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“…Sun et al proposed an optical flow with detection algorithm based on color space to detect the objects in shadow. e model enables detecting in daytime with high shadow and gives high accuracy [7,13]. However, the model required long time to make frame removal computations.…”

“…e probability of choosing a centroid will be directly proportional to the nearest distance. e k-mean is used on various k on vehicle sizes and aspect ratios to find the best k to increase the mAP [13].…”

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“…Many previous researchers have used the optical flow in the vehicle recognition field. One of which is Sun et al [10], who use it to detect and track vehicles in complex traffic conditions with shadow and occlusion. This system combines optical flow and immune particle filter, which increases tracking reliability to work well even in low visibility conditions.…”

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