Publication

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).