Abnormal Pedestrian Trajectory analysis based on arbitrary-length clustering

This papers examines the use of trajectory distance measures and clustering techniques to define normal<br/>and abnormal trajectories in the context of pedestrian tracking in public spaces. In order to detect abnormal<br/>trajectories, what is meant by a normal trajectory in a given scene is firstly defined. Then every trajectory<br/>that deviates from this normality is classified as abnormal. By combining Dynamic Time Warping and a<br/>modified K-Means algorithms for arbitrary-length data series, we have developed an algorithm for trajectory<br/>clustering and abnormality detection. The final system performs with an overall accuracy of 83% and 75%<br/>when tested in two different standard datasets.<br/>