Abstract
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.
Highlights
The area of human motion analysis is one of the most active research areas in computer vision, with applications in numerous fields such as surveillance, content-based retrieval, storage, virtual reality and many others
Feature-based methods are sensitive to local noise and occlusions, and the number of features is not always sufficient for tracking or recognition. Statistical shape models such as Active Contours have been examined for human motion analysis [11], but they are sensitive to occlusions and require good initialization
Motion Energy Images (MEIs) are binary masks indicating which pixels are active throughout the video, while Motion History Images (MHIs) are grayscale, as they incorporate history information, i.e. which pixels moved most recently
Summary
The area of human motion analysis is one of the most active research areas in computer vision, with applications in numerous fields such as surveillance, content-based retrieval, storage, virtual reality and many others. Feature-based methods are sensitive to local noise and occlusions, and the number of features is not always sufficient for tracking or recognition Statistical shape models such as Active Contours have been examined for human motion analysis [11], but they are sensitive to occlusions and require good initialization. Another large category of approaches extracts cues about the activity taking place from motion information [12]. Motion Energy Images (MEIs) are binary masks indicating which pixels are active throughout the video, while Motion History Images (MHIs) are grayscale, as they incorporate history information, i.e. which pixels moved most recently This approach is computationally efficient, but cannot deal with repetitive actions, as their signatures overwrite each other in the MHI. It does not require extensive training for recognition, so it is not computationally intensive, nor dependent on the training data available
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