Semantic Modelling for Behaviour Characterisation and Threat Detection

Luis Patino, James Ferryman; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 43-49

Threat detection in computer vision can be achieved by extraction of behavioural cues. To achieve recognition of such cues, we propose to work with Semantic Models of behaviours. Semantic Models correspond to the translation of Low-Level information (tracking information) into High-Level semantic description. The model is then similar to a naturally spoken description of the event. We have built semantic models for the behaviours and threats addressed in the PETS 2016 IPATCH dataset. Semantic models can trigger a threat alarm by themselves or give situation awareness. We describe in this paper how semantic models are built from Low-Level trajectory features and how they are recognised. The current results are promising.