ViBe: A Universal Background Subtraction Algorithm for Video Sequences

Abstract

Abstract-This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based on the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel.We describe our method in full details (including pseudocode and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques. An implementation of ViBe is available at http://www.motiondetection.org.

The Visual Object Tracking VOT2014 Challenge Results

“…Submitted by VOT Committee SAMF tracker is based on the idea of correlation filterbased trackers [15,27,26,5] with aim to improve the overall tracking capability. To tackle the problem of the fixed template size in kernel correlation filter tracker, an effective scale adaptive scheme is proposed.…”

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

“…Vibe [16], is another interesting non parametric approach in which the pixel model is based on a set of background samples (including past and neighbor pixels), instead of being based on an explicit pixel background distribution model. Each incoming pixel is compared with the closest sample in the background set and those that match the background are then included in the set.…”

“…The Vibe algorithm [16] is applied independently to the color and the depth data: the obtained foreground masks are then combined with logical operations and then post processed with morphological operations.…”

“…(10)) that has to be initialized with the very first frames. In particular for CL C the likelihood parameters p. and £ are initialized with the first frame by analyzing neighbor pixels and supposing that they share the same distribution as proposed in [29] and successfully used in [16]. Also in the case of CL D the parameters of the onedimensional likelihood function are initialized with the first frame: the value of p. corresponds to the measured depth value and the value of a is selected according to the distance-noise quadratic law (see [10]), in particular for each measured depth we theoretically know its dispersion {a noise ).…”

Subsequent Processing of Background Modeling for Traffic Surveillance System

“…Fuzzy is also implemented to the background subtraction, each step must be designed and the features in relation has to be chosen in relation to the critical situation [7], [8]. Moreover, the ViBe [9] is a technique for motion detection that incorporates several innovative mechanisms. To conclude, Surveys and comparisons of different algorithms for background subtraction can be found in the literature [10]- [12].…”