A Motion Target Detection Algorithm with Video Processing Capabilities

This algorithm enables users to load various video formats (e.g., MP4, AVI, MOV) through a file handling module that utilizes OpenCV's VideoCapture class. The core implementation involves frame differencing techniques where consecutive frames are compared using absolute difference calculations, followed by morphological operations to reduce noise. Key functions include: - VideoReader() for initializing video streams - BackgroundSubtractorMOG2() for adaptive background modeling - findContours() for detecting moving object boundaries The algorithm operates through three main stages: background initialization, foreground segmentation, and object tracking. It outperforms basic threshold-based methods by incorporating Gaussian mixture models that adapt to lighting changes. Typical applications include surveillance systems, traffic monitoring, and autonomous navigation. Compared to optical-flow-based approaches, this method offers better computational efficiency while maintaining accuracy through spatial-temporal analysis. Limitations include sensitivity to sudden illumination changes, which can be mitigated by implementing histogram equalization preprocessing. Future developments may integrate deep learning architectures like YOLO for improved detection precision. The algorithm's history stems from classical computer vision research, with recent enhancements focusing on GPU acceleration using CUDA kernels. Developers can extend functionality by modifying the detection sensitivity parameters or integrating custom filters through the provided API hooks.