| Interest in the field of human activity recognition has existed for quite sometime, but has gained popularity in recent years for use in many areas of application. In the security industry, suspicious activities could be detected in high-profile areas. In the medical industry, systems could be trained to detect patterns of motion indicating distress or to detect a lack of motion if a person had fallen and was unable to move. However, algorithms with reliable accuracy are difficult to implement in a real-time environment due to computational complexity.; This thesis developed a new way of extracting and using data from a human figure in a video frame to determine what type of activity the subject is performing. Following background subtraction, a thinning algorithm operating on the silhouette offered a more robust limb extraction method, while a six-segment representation of the human figure offered more accuracy in deriving limb parameters, or components, such as distance from torso, and angle of displacement from the vertical axis. Neural networks or nearest neighbor classifiers used the limb components to identify a number of activities, such as walking, running, waving and jumping. This entire human activity recognition system was tested with both a MATLAB implementation (non real-time) and a C++ implementation in OpenCV (real-time). The algorithm achieved 96% classification accuracy in video feeds, which is only slightly lower than that of intensive, non real-time systems. |
