Title :
Total Occlusion Correction using Invariantwavelet Features
Author :
Ghazal, Mohammed ; Amer, Aishy
Author_Institution :
Concordia Univ., Montreal
fDate :
Sept. 16 2007-Oct. 19 2007
Abstract :
This paper proposes a method which utilizes invariant wavelet features for correcting total occlusion in video surveillance applications. The proposed method extracts invariant wavelet features from the pre-occlusion spatial image of disappearing objects. When new objects are detected during occlusion, their extracted invariant wavelet features are compared to those of lost objects to check for reappearance. When reappearance occurs, the proposed method rebuilds the correct correspondence map between pre-occlusion and post occlusion objects to continue to track the ones that were lost during total occlusion. Our results show that the proposed method is more robust than referenced methods especially when objects change or reverse their motion direction during occlusion.
Keywords :
feature extraction; hidden feature removal; object detection; video surveillance; wavelet transforms; feature extraction; invariant wavelet features; object detection; total occlusion correction; video signal processing; video surveillance; Application software; Cameras; Computer vision; Data mining; Layout; Object detection; Robustness; Tracking; Video sequences; Video surveillance; Tracking; video signal processing; wavelet transforms;
Conference_Titel :
Image Processing, 2007. ICIP 2007. IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
978-1-4244-1437-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2007.4379317