Author:
Andrzej Sluzek
Affiliation:
Khalifa University, United Arab Emirates
Keyword(s):
Visual Surveillance, CBVIR, Real Time, Keypoint Matching, Keypoint Descriptors, MSER, SIFT.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Real-Time Systems Control
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Surveillance
;
Vision, Recognition and Reconstruction
Abstract:
CBVIR approach to video-based surveillance is discussed. The objective is to detect in real time near-duplicates (e.g. similarly-looking objects) simultaneously appearing in concurrently captured/played videos. A novel method of keypoint matching is proposed, based on keypoint descriptions additionally incorporating visual and geometric contexts. Near-duplicate fragments can be identified by keypoint matching only. The analysis of geometric constraints (a bottleneck of typical CBVIR methods for sub-image retrieval) is not required. When the proposed method is fully implemented, high-speed and good performances can be achieved, as preliminarily shown in proof-of-concept experiments. The method is affine-invariant and employs typical keypoint detectors and descriptors (MSER and SIFT) as the low-level mechanisms.