Human Motion Analysis via Statistical Motion Processing and Sequential Change Detection

The widespread use of digital multimedia in applications, such as security, surveillance, and the semantic web, has made the automated characterization of human activity necessary. In this work, a method for the characterization of multiple human activities based on statistical processing of the video data is presented. First the active pixels of the video are detected, resulting in a binary mask called the Activity Area. Sequential change detection is then applied to the data examined in order to detect at which time instants there are changes in the activity taking place. This leads to the separation of the video sequence into segments with different activities. The change times are examined for periodicity or repetitiveness in the human actions. The Activity Areas and their temporal weighted versions, the Activity History Areas, for the extracted subsequences are used for activity recognition. Experiments with a wide range of indoors and outdoors videos of various human motions, including challenging videos with dynamic backgrounds, demonstrate the proposed system's good performance.