Background modelling by codebook technique for Automated Video Surveillance with Shadow Removal

Rabha, Jharana Rani

doi:10.1109/icsipa.2015.7412258

Cited by 2 publications

(1 citation statement)

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“…In the last few years many shadow removal approaches have been introduced to improve detection ratio of state of the arts algorithms by using gradient amendment, edge-based, histogram, etc. [49][50][51] These methods usually face with some complex situation such as lack of information in darkness or low light condition and completely change of chromatic properties of the context.…”

Section: Related Workmentioning

confidence: 99%

Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

Dorudian

Lauria

Swift

2019

International Journal of Micro Air Vehicles

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A novel approach to detect micro air vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving micro air vehicles by combining colour and depth information. The proposed method stores several colour and depth images as models and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background model update presented uses different criteria from existing methods. Additionally, a blind update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different micro air vehicles and publicly available datasets. Results showing some improvements over existing methods are discussed.

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Section: Related Workmentioning

confidence: 99%