  We present an improved clustering based, unsupervised anomalous trajectory
detection algorithm for crowded scenes. The proposed work is based on four
major steps, namely, extraction of trajectories from crowded scene video,
extraction of several features from these trajectories, independent mean-shift
clustering and anomaly detection. First, the trajectories of all moving objects
in a crowd are extracted using a multi feature video object tracker. These
trajectories are then transformed into a set of feature spaces. Mean shift
clustering is applied on these feature matrices to obtain distinct clusters,
while a Shannon Entropy based anomaly detector identifies corresponding
anomalies. In the final step, a voting mechanism identifies the trajectories
that exhibit anomalous characteristics. The algorithm is tested on crowd scene
videos from datasets. The videos represent various possible crowd scenes with
different motion patterns and the method performs well to detect the expected
anomalous trajectories from the scene.
