Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 347-350, 2014
https://doi.org/10.5194/isprsarchives-XL-3-347-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
11 Aug 2014
Fast Moving Objects Detection Using iLBP Background Model
B. Vishnyakov, V. Gorbatsevich, S. Sidyakin, Y. Vizilter, I. Malin, and A. Egorov State Research Institute of Aviation Systems (FGUP GosNIIAS), Moscow, Russia
Keywords: Background model, LBP descriptor, iLBP descriptor, regression model, video surveillance, object detection 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.
Conference paper (PDF, 412 KB)


Citation: Vishnyakov, B., Gorbatsevich, V., Sidyakin, S., Vizilter, Y., Malin, I., and Egorov, A.: Fast Moving Objects Detection Using iLBP Background Model, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 347-350, https://doi.org/10.5194/isprsarchives-XL-3-347-2014, 2014.

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