Vehicle Detection and Tracking for Video Sequences

Application Background

This project focuses on detecting and tracking vehicles in video sequences. We implement the background subtraction method for vehicle detection and Kalman filtering for vehicle tracking. The detection results are visualized using red bounding boxes, while tracking results are indicated with green bounding boxes. The code package includes a sequence of motion vehicle frame images for demonstration. To maintain background image accuracy, we employ an average background method for continuous background updates. The code contains comprehensive comments addressing potential implementation questions.

Key Technologies

The core technologies include background subtraction and Kalman filtering algorithms. For vehicle detection, the background subtraction method compares current frame differences with the background model to identify vehicle positions. The tracking module utilizes Kalman filtering to predict vehicle motion trajectories. The implementation uses distinct colored bounding boxes (red for detection, green for tracking) for clear visualization of results. The code structure separates detection and tracking modules, with background modeling implemented through frame averaging techniques. Detailed function descriptions cover parameter configuration for optimal performance in varying scenarios.