ABSTRACT:In this paper, we propose a new approach for moving objects detection in video surveillance systems. It is based on construction of the regression diffusion maps for the image sequence. This approach is completely different from the state of the art approaches. We show that the motion analysis method, based on diffusion maps, allows objects that move with different speed or even stop for a short while to be uniformly detected. We show that proposed model is comparable to the most popular modern background models. We also show several ways of speeding up diffusion maps algorithm itself.
An original method for object detection based on morphlet trees is proposed in the paper. It allows the robust detection of heterogeneous objects in images to be done without pre-training. Besides, the detection process simultaneously includes a preliminary segmentation, which can be later used for recognition. Also, there is another important characteristic: the proposed approach does not require the use of sliding windows and feature pyramids to detect different-scale objects.
ABSTRACT:In this paper a new approach for moving objects detection in video surveillance systems is proposed. It is based on iLBP (intensity local binary patterns) descriptor that combines the classic LBP (local binary patterns) and the multiple regressive pseudospectra model. The iLBP descriptor itself is considered together with computational algorithm that is based on the sign image representation. We show that motion analysis methods based on iLBP allow uniformly detecting objects that move with different speed or even stop for a short while along with unattended objects. We also show that proposed model is comparable to the most popular modern background models, but is significantly faster.
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