Machine Vision Course: Moving Object Detection

Moving object detection is a fundamental task in the field of machine vision and a common practical course project at the graduate level. It primarily addresses the problem of identifying and extracting moving objects from video sequences, with widespread applications in security surveillance, intelligent transportation systems, and other domains.

The core implementation approach typically involves several key stages: First, background modeling is required, where techniques like Gaussian Mixture Models (GMM) or frame differencing methods establish a reference baseline for static backgrounds. Then, a foreground mask is calculated by comparing the current frame with the background model, using threshold segmentation to initially extract motion regions. Subsequently, morphological processing techniques (such as erosion and dilation operations) are applied to eliminate noise interference, followed by connected component analysis to identify independent moving objects. For multi-object scenarios, object tracking algorithms (like Kalman filters or correlation trackers) must be implemented to maintain consistent association of objects across frames.

In practical course projects, students can compare the detection performance differences between traditional algorithms (such as optical flow methods implemented with OpenCV's calcOpticalFlowPyrLK function) and deep learning approaches (like YOLO combined with DeepSORT tracking). Evaluation metrics should be designed to quantitatively analyze algorithm robustness under challenging conditions like illumination changes and occlusions. The experimental phase should incorporate both public datasets (such as MOT Challenge) and self-recorded videos for comprehensive validation.

This project systematically trains core machine vision capabilities including image processing, feature extraction, and temporal analysis, serving as a典型典型 representative case for understanding dynamic scene analysis. Implementation typically involves OpenCV functions like BackgroundSubtractorMOG2 for background modeling, findContours for blob detection, and custom Python/Matlab code for performance evaluation.