Open AccessDetection of salient regions in crowded scenes
Author(s) -
Lim M.K.,
Chan C.S.,
Monekosso D.,
Remagnino P.
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2013.3993
Subject(s) - salient , computer science , computer vision , artificial intelligence , context (archaeology) , task (project management) , field (mathematics) , motion (physics) , dynamics (music) , stability (learning theory) , flow (mathematics) , optical flow , image (mathematics) , machine learning , geography , engineering , mathematics , physics , geometry , archaeology , systems engineering , acoustics , pure mathematics
The increasing number of cameras and a handful of human operators to monitor the video inputs from hundreds of cameras leave the system ill equipped to fulfil the task of detecting anomalies. Thus, there is a dire need to automatically detect the regions that require immediate attention for more effective and proactive surveillance. A framework that utilises the temporal variations in the flow field of a crowd scene to automatically detect salient regions is proposed, while eliminating the need to have prior knowledge of the scene or training. The flow fields are deemed to be a dynamic system and adopt the stability theory of dynamic systems, to determine the motion dynamics within a given area. In this context, the salient regions refer to the areas with high motion dynamics, where the points in a particular region are unstable. The experimental results on public, crowd scenes have shown the effectiveness of the proposed method in detecting salient regions which correspond to an unstable flow, occlusions, bottlenecks, and entries and exits.