Anomaly Detection and Activity Perception Using Covariance Descriptor for Trajectories
In this work, we study the problems of anomaly detection and activity perception through the trajectories of objects in crowded scenes. For this purpose, we propose a novel representation for trajectories via covariance features. Representing trajectories
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Electrical and Electronics Engineering, METU, Ankara, Turkey {hamza.ergezer,kleb}@metu.edu.tr MGEO Division, EO System Design Department, Aselsan Inc., Ankara, Turkey [email protected]
Abstract. In this work, we study the problems of anomaly detection and activity perception through the trajectories of objects in crowded scenes. For this purpose, we propose a novel representation for trajectories via covariance features. Representing trajectories via feature covariance matrices enables us to calculate the distance between the trajectories of different lengths. After setting this proposed representation and calculation of distances between trajectories, anomaly detection is achieved by sparse representations on nearest neighbors and activity perception is achieved by extracting the dominant motion patterns in the scene through the use of spectral clustering. Conducted experiments show that the proposed method yields results which are outperforming or comparable with state of the art. Keywords: Covariance features detection · Activity perception
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Trajectory analysis
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Anomaly
Introduction
Improvements in camera technology make the video surveillance systems easily accessible. For this reason, application areas of video surveillance systems are broad. Together with this progress, user expectations have induced new challanges to the field. The biggest challange is that automated handling of some tasks became mandatory for surveillance systems. Activity perception and anomaly detection are among those important tasks for surveillance systems. Many approaches have been proposed in literature for anomaly detection and activity perception in scenes. These approaches generally differ from each other with respect to the visual features they utilize. Despite some difficulties in the extraction stage, especially in crowded scenes, trajectory is still one of the most useful features for an object of interest. Trajectory is 2D or 3D time series data depending on application. It carries position information of the moving object with respect to time. Other valuable information such as velocity can also be derived from trajectory data. Therefore, trajectory data is crucial for several surveillance applications. In maritime surveillance, trajectory of a vessel is the biggest clue about its behaviour. A hijacked c Springer International Publishing Switzerland 2016 G. Hua and H. J´ egou (Eds.): ECCV 2016 Workshops, Part II, LNCS 9914, pp. 728–742, 2016. DOI: 10.1007/978-3-319-48881-3 51
Covariance Descriptor for Trajectories
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plane can be identified from its trajectory in aviation surveillance. For video surveillance, trajectories of the objects in the scene gives information about motion patterns. Also, trajectory of a high speed car will be different from others and can be identified as an anomaly. As can be seen from the examples, trajectories are valuable features of moving objects to handle tasks such as anomaly detection and activity perception. In this work, a novel descriptor is proposed for trajectories us
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