Adaptive Background Modeling Integrated With Luminosity Sensors and Occlusion Processing for Reliable Vehicle Detection

This paper presents a novel vehicle detection and tracking system with stationary camera that relies on a recursive background-modeling approach, i.e., the adaptive Poisson mixture model, which is integrated with a hardware module consisting of luminosity sensors. The luminosity information side channel allows the system to effectively handle rapid changes in illumination, which is typical of outdoor applications and bottleneck of the existing background pixel classification methods. A novel algorithm for detecting and removing partial and full occlusions among blobs is also proposed. Partial occlusions are detected by evaluating the ratio between the area of the vehicle and the area of the vehicle's convex hull and are suppressed by identifying a cutting line using curvature analysis. A predictive model of the shape and motion features of the vehicles over consecutive frames instead corrects the error of the previous levels when full occlusions or background-vehicle occlusions occur in the scene. Quantitative evaluation and comparisons on some real-world scenarios demonstrate that the proposed approach outperforms state-of-the-art methods in terms of both vehicle detection and processing time, particularly due to the robustness and the efficiency of the background-modeling algorithm.