Koetsier - Research Projects

Master Theses

  • Trajectory anomaly detection using spectral clustering and RNN-based auto-encoder
    Anomaly detection is important, because anomalous behavior may indicate critical events or objects within diverse research areas and application domains. One of such domains is transport, especially integrated urban mobility. Trajectories of moving objects are good representations of their behaviors in surveillance data and useful in detecting anomalous behavior. On one hand, trajectories can provide more agent-based, long-term information comparing with simple physical features. On the other hand, comparing with raw video data, which is usually represented as a sequence of images, trajectory data requires less storage space and computational resources. Moreover, it has a wide variety of sources, such as GPS instruments and laser-scanners.
    Leaders: Sester, Koetsier
    Team: Yao Li
    Year: 2020