Background Image Acquisition, Differencing, Binarization, and Target Extraction
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
In this process, we first extract 100 frames from the input video sequence. These frames serve as the basis for generating the background image. Using 50 selected frames, we apply the average method to compute the background image by calculating the pixel-wise mean across the frames. This approach effectively reduces noise and minimizes the influence of moving objects. Subsequently, we perform differencing by subtracting the background image from each current frame to highlight foreground objects. The resulting difference image then undergoes binarization using an optimal threshold (determined through methods like Otsu's algorithm or adaptive thresholding) to convert it into a binary mask. Finally, target extraction is achieved through contour detection or connected component analysis, enabling accurate isolation and identification of objects of interest. This pipeline requires precise parameter tuning and validation to ensure optimal detection results.
- Login to Download
- 1 Credits