Trajectory Analysis in Surveillance

Erik Lundin

Surveillance is a steadily increasing phenomenon and the used sensors -- cameras, radars etcetera -- produce more and more data that has to be examined. To sort and value the information, systems are needed that automatically finds interesting behaviors for the operators to have a closer look at. This project aim to examine methods to analyse the traces -- trajectories -- that are created when object move through a surveilled area. Trajectory clustering, anomaly detection and prediction of future behaviour are desirable features of a system to assist an operator. Several methods, based on vector quantisation, hidden Markov modeling and trajectory clustering are studied and two of them are implemented. These implementations aim at test the performance of some interesting methods, and to give hint of what software is capable to do in a surveillance context. The results show that many of the desired features are possible to implement, but more research in the area is needed to take these features closer to a large scale system.

• Pobulärvetenskaplig sammanfattning (Swedish)