TY  - CHAP
AU  - Pau Baiget
AU  - Carles Fernandez
AU  - Xavier Roca
AU  - Jordi Gonzalez
PY  - 2012//
TI  - Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance
BT  - Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence
SP  - 87
EP  - 95
VL  - 384
IS  - 3
PB  - Springer Berlin Heidelberg
N2  - The recognition of abnormal behaviors in video sequences has raised as a hot topic in video understanding research. Particularly, an important challenge resides on automatically detecting abnormality. However, there is no convention about the types of anomalies that training data should derive. In surveillance, these are typically detected when new observations differ substantially from observed, previously learned behavior models, which represent normality. This paper focuses on properly defining anomalies within trajectory analysis: we propose a hierarchical representation conformed by Soft, Intermediate, and Hard Anomaly, which are identified from the extent and nature of deviation from learned models. Towards this end, a novel Gaussian Mixture Model representation of learned route patterns creates a probabilistic map of the image plane, which is applied to detect and classify anomalies in real-time. Our method overcomes limitations of similar existing approaches, and performs correctly even when the tracking is affected by different sources of noise. The reliability of our approach is demonstrated experimentally.
SN  - 1860-949X
SN  - 978-3-642-24033-1
L1  - http://refbase.cvc.uab.es/files/BFR2012.pdf
UR  - http://dx.doi.org/10.1007/978-3-642-24034-8_7
N1  - ISE
ID  - Pau Baiget2012
ER  -