Anomaly Detection in Trajectories


24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey, 16 - 19 May 2016, pp.1561-1564 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • City: Zonguldak
  • Country: Turkey
  • Page Numbers: pp.1561-1564
  • Keywords: anomaly detection, trajectory representation, covariance feature


In this work, we study the problem of anomaly detection of the trajectories of objects in a visual scene. For this purpose, we propose a novel representation for trajectories utilizing covariance features. Representing trajectories via covariance features enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances, anomaly detection is achieved by sparse representations on nearest neighbours. Conducted experiments on both synthetic and real datasets show that the proposed method yields results which are outperforming or comparable with state of the art.