Morphological Background Model for the Analysis of Traffic Flows in Urban Areas

The increasing complexity of urban infrastructure and mobility has driven the need for new technologies to monitor traffic efficiently in urban settings. Automated monitoring systems assisted by computer vision are tools that help streamline traffic surveillance in these environments. Computer vision systems rely on the accurate detection of moving objects. This article presents the development of a background model algorithm based on mathematical morphology for detecting and analyzing objects in videos captured by a drone with a top-down view at roundabouts. The proposed methodology uses a morphological process on grayscale images to highlight textures and distinctive characteristics of the scene. Subsequently, the expected pixel intensity is estimated using statistical mode, which allows the proposed model to rely exclusively on integer operations. By operating solely on integer logic, the approach enhances computational performance and avoids the limitations associated with floating-point calculations. This feature enables efficient performance on minimal architectures with low energy consumption. The algorithm’s experimental implementation and evaluation demonstrate its effectiveness in robust object detection in both static and dynamic scenarios, remaining unaffected by variations in object perspective and changes in scene lighting. This approach contributes to developing efficient and non-intrusive solutions for urban traffic analysis, facilitating decision-making in the planning and management of mobility.