Grouping multi-vector streaklines for human activity identification

In this study, human activity identification is approached as a record, analyze and model from the video sequence as you observe the scene in time methodology. The computational approach has two stages: training and identification. During the training stage, specific human activities are identified and characterised by employing modelling of medium-term movement flow through streaklines. Each streak-lines is formed by multiple optical flow vectors that represent and track locally the movement in the scene. A dictionary of activities is recorded for a given scene during the training stage. During the testing stage, the consistency of each observed activity with those from the dictionary is verified using the Kullback-Leibler (KL) divergence. Moving regions that are not present in the dictionary are identified, triggering decisions such as those specific to anomalous human activities.