Pedestrian Detection and Tracking in Videos Using Frame Difference Method from Motion Target Detection

In this article, the author presents an approach using the frame difference method from motion target detection to detect and track pedestrians in videos, achieving notably good results. The frame difference algorithm typically involves calculating the absolute difference between consecutive video frames, then applying thresholding to identify moving regions. While this method has proven effective, several additional factors need consideration for comprehensive understanding. For instance, we must examine the practical application scenarios and whether the method maintains effectiveness across different environments. Furthermore, in real-world implementations, modifications and adjustments might be necessary to better suit specific requirements - such as optimizing threshold values, implementing morphological operations for noise reduction, or incorporating background subtraction techniques. When learning and adopting this approach, it's crucial to thoroughly evaluate both its advantages and limitations, while attempting practical improvements through code optimization and parameter tuning. Only through such comprehensive consideration and practical refinement can we better apply this method and achieve superior results in pedestrian detection and tracking applications.