Background subtraction: basic methods, mean and variance over time

Performance map

BenchmarkBasic

Static Frame Difference

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{gallery}StaticFrameDifferenceBGS{/gallery}

Frame Difference

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{gallery}FrameDifferenceBGS{/gallery}

Weighted Moving Mean

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{gallery}WeightedMovingMeanBGS{/gallery}

Weighted Moving Variance

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{gallery}WeightedMovingVarianceBGS{/gallery}

Adaptive Background Learning

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{gallery}AdaptiveBackgroundLearning{/gallery}

Temporal Mean

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{gallery}DPMeanBGS{/gallery}

Adaptive Median of McFarlane and Schofield (1995) paper link

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An algorithm was developed for the segmentation and tracking of piglets and tested on a 200-image sequence of 10 piglets moving on a straw background. The image-capture rate was 1 image/140 ms. The segmentation method was a combination of image differencing with respect to a median background and a Laplacian operator. The features tracked were blob edges in the segmented image. During tracking, the piglets were modelled as ellipses initialised on the blobs. Each piglet was tracked by searching for blob edges in an elliptical window about the piglet’s position, which was predicted from its previous two positions.

{gallery}DPAdaptiveMedianBGS{/gallery}

Temporal Median of Cucchiara et al (2003) and Calderara et al (2006) paper link1 paper link2 paper link3

{youtube}F0iikjswKK8{/youtube}

Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. The article proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.

{gallery}DPPratiMediodBGS{/gallery}

 

These algorithms are contained in the bgslibrary by Andrews Sobral, that includes over 30 background subtraction algorithms, a common C++ framework for comparing them, and an handy C++/MFC or Java app to see them running on video files or live feed from a webcam.

Return to the list of background subtraction algorithms

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