This paper presents a proposal to manage simple-objects interaction in video surveillance system. The proposal consists on locating a set of features in each video frame. Maxima regions from the second Eigen- value of the tensor matrix are used as features. Afterwards, statics features are discarded (labeling as background) and dynamic features are used to represent objects in motion (foreground). Dynamics features are dynamically clustered with k-neighborhood and EM algorithm. The centroid of each cluster locally represents motion objects, and its displacement through time is denoted by displacement of cumulus over several frames. The behavior of cumulus in time help us to model simple object interactions. These primitives can be used in addition to a causal dependencies across time; i.e. cluster division, cluster fusion and cluster motion with respect to the others, offer information of local dynamics which is referred to local interactions. And based on causal dependencies theory, a graph dependence of local centroids behavior can be built. This graph can represent the local interaction model. In experimental section, the approach is tested in several scenarios, extracting simple interaction objects in controlled/not-controlled scenarios.

Date Published: 4 March 2015
PDF: 9 pages
Proc. SPIE 9407, Video Surveillance and Transportation Imaging Applications 2015, 94070V (4 March 2015); doi: 10.1117/12.2080827

Published in SPIE Proceedings Vol. 9407:
Video Surveillance and Transportation Imaging Applications 2015
Robert P. Loce; Eli Saber, Editor(s)