Research on Automatic Detection Method of Basketball Flight Trajectory Based on Background Subtraction

Accurate trajectory detection of moving objects is of considerable importance for intelligent visual sensing and electromagnetic-assisted monitoring systems, where reliable feature extraction and signal interpretation directly affect perception performance. To achieve precise detection of basketball flight trajectories, this study proposes an automatic detection method based on background subtraction. The proposed framework first establishes the operational procedure of the background subtraction algorithm, employs a multi-feature fusion strategy to suppress motion shadows, and applies median filtering for image denoising. A dynamically updated background reference model is then constructed to maintain robustness under changing scene conditions. Subsequently, the gray-scale difference image is binarized using an entropy maximization criterion to determine the optimal segmentation threshold, thereby extracting the foreground target and accurately identifying the basketball flight trajectory. Experimental results demonstrate that the proposed method significantly improves both detection accuracy and real-time performance while reducing missed detections and false alarms compared with conventional approaches. The developed framework provides an effective solution for trajectory analysis in dynamic environments and offers useful technical insights for electromagnetic sensing, intelligent imaging, and signal processing applications requiring robust moving-object detection.