Detecting illegally parked vehicle based on cumulative dual foreground difference

As one of the traffic monitoring tasks, detecting an illegally parked vehicle aims to prevent car crashing between parked and other vehicles. However, developing such a task becomes more complex due to weather conditions, occlusion, illumination changing, and other factors. This work addresses a framework to detect an illegally parked vehicle using a cumulative dual foreground difference. In our framework, two background models with different learning rates are generated based on a Gaussian mixture model, defined as short- and long-term models. Each model extracts foreground pixels and the stability of these pixels are then analyzed based on cumulative values and temporal positions over a certain period of time. Subsequently, the connected component labeling is performed on the static pixels to form stable regions. To determine whether the candidate region is vehicle, a rule-based filtering approach is performed. Finally, the detection-based tracking is applied to reduce false positives. The effectiveness of the proposed framework is evaluated using i-LIDS and ISLab dataset. The experiment results show that the proposed framework is efficient and robust to detect an illegally parked vehicle. Thus, it can be considered as one of the task solutions for a traffic monitoring system.