Abstract:

The scene in a video sequence changes in real time， sometimes the foreground target changes together with the background， and sometimes the foreground target changes while the background remains unchanged. It is very difficult to achieve detection and tracking of foreground targets. To this end， a high-speed moving object detection algorithm based on Gaussian kernel density estimation is proposed. Using Gaussian kernel density estimation to establish a background model and obtain the probability density distribution of each pixel point； Extract keyframes containing high-speed moving targets from the video sequence and calculate the weight of each keyframe's grayscale value； Using a full sample timing and real-time selective update strategy to update the background model， and using the updated model to achieve accurate detection of high-speed moving targets. The high-speed motion object detection was carried out on a certain video segment in the highwayI-raw standard test sequence， and the results showed that the proposed method has high detection accuracy.

Key words: Gaussian kernel density estimation, high speed moving target detection, probability density distribution, background model, keyframes