Drawing Red Rectangular Markers at Specified Positions on Images

In computer vision and image processing, annotating position information for targets is a common requirement. Drawing red rectangular boxes on images to mark specific target regions provides intuitive visual references for subsequent object tracking algorithms.

Implementing this functionality typically involves several key steps: First, the original image data needs to be loaded into the program using functions like cv2.imread() in OpenCV or imread() in MATLAB. Then, the coordinates of the target region to be marked must be determined. These coordinates are usually represented either as (x,y) pairs for the top-left and bottom-right corners, or as top-left coordinates plus width and height values. Next, drawing functions from image processing libraries (such as cv2.rectangle() in OpenCV or rectangle() in MATLAB) are used to draw rectangular outlines with specific colors and line widths at the designated coordinate positions. Red is the most commonly used marker color because it remains highly visible against most backgrounds.

For object tracking applications, these markers not only display detection results in the current frame but, more importantly, provide visual representation of the algorithm's tracking performance throughout the entire video sequence. By comparing the position changes of rectangular boxes across consecutive frames, developers can quickly evaluate the performance of tracking algorithms. Implementation-wise, this typically involves creating a loop that processes each frame, updates bounding box coordinates using tracking algorithms like KCF or MOSSE, and redraws the rectangles accordingly.