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Improving Change Detection Using Centre-Symmetric Local Binary Patterns

Publication Type : Conference Paper

Publisher : Pattern Recognition and Machine Intelligence

Source : Pattern Recognition and Machine Intelligence, Springer International Publishing, Cham (2019)

ISBN : 9783030348724

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2019

Abstract : Efficient change detection in real-time applications is a major research goal in computer vision. Several researchers have put efforts in this direction and have achieved notable performances in varied challenging situations. But handling all the challenges posed in real-time environments with a single change detection method is almost impracticable. On the other hand, ensemble based background modelling techniques have obtained improved results but they also suffer from trade-off between efficiency and hardware or time requirements, thereby hindering their real-time applicability. This paper proposes an effective hybrid change detection algorithm, light and simple enough to have an effective real-time applicability. The proposed hybrid change detection algorithm employs per-channel RGB colour features with centre-symmetric local binary patterns for pixel-modelling and feeds it to a sample-consensus classification technique for foreground segmentation. Finally, performance of the proposed technique has been tested on widely accepted change detection dataset namely, 2014 Change detection dataset (2014 CDnet dataset).

Cite this Research Publication : Rimjhim Padam Singh and Sharma, P., “Improving Change Detection Using Centre-Symmetric Local Binary Patterns”, in Pattern Recognition and Machine Intelligence, Cham, 2019.

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