A combination of new static detection and dynamic detection in people counting
Xuan Zhou, Shan Zhen Xu, Yuanhao Wang · 2012
With the improvement of the intelligence degree of video surveillance system, the people-counting in surveillance area becomes a studying focus. This paper proposes a fast and effective people-counting method. In this method, we divide the monitoring area into blocks at first, and then we recognize people in each block and combine both the static detection and the dynamic detection. In the static detection part, we use covariance algorithm to avoid factitious adjustment of threshold. As a result, it will be more persuasive by comparing the entire difference distributions. What is more, some interference of non-human objects can be excluded effectively by using the dynamic detection. Therefore we can get a more accurate final result.