Motion Object Detection: Foundation for Video Tracking Technology with MATLAB Implementation

In video processing, motion object detection constitutes one of the fundamental bases for subsequent tracking technologies. To effectively track moving targets and enhance tracking accuracy, motion detection must be performed, where the quality of detection results directly impacts subsequent tracking processes. Motion object detection refers to the process of extracting foreground, motion patterns, and targets from sequential images obtained by machine vision systems. This article presents main methods for motion detection in computer vision, specifically introducing several typical background subtraction algorithms, their principles, characteristics, and the basic workflow of background differencing method comprising four key steps: preprocessing, background modeling, target detection, and post-processing. Furthermore, we implement background subtraction algorithms in MATLAB environment to complete motion detection in videos and perform additional processing on the resultant detection images.

Background subtraction represents a widely employed approach in motion object detection. Its fundamental concept involves distinguishing background pixels from foreground pixels by establishing a background model. Currently, various background subtraction methods are commonly used, such as Gaussian Mixture Model (GMM)-based background subtraction, adaptive Gaussian mixture model-based approaches, and sparse coding-based background subtraction techniques. These algorithms possess distinct characteristics and can be selected according to practical requirements. In MATLAB implementation, the vision.ForegroundDetector system object with GMM parameters provides an efficient way to model background variations while accounting for multimodal distributions.

Background subtraction finds extensive application in motion detection. In this study, we employ Gaussian Mixture Model-based background subtraction to detect moving targets in videos. Specifically, we first perform video preprocessing including grayscale conversion and size normalization using im2gray and imresize functions. Then we establish a background model to differentiate background pixels from foreground pixels through iterative model updating. Subsequently, we conduct target detection on each frame using thresholding operations and morphological processing, followed by post-processing steps like noise removal and gap filling using imopen and imclose functions to further improve detection accuracy. Finally, we obtain a series of motion detection result images that can be utilized for subsequent applications such as object tracking and behavior analysis.