Machine Learning on data with sPlot background subtraction

Background Subtraction on Distributions

Environmental monitoring applications present a challenge to current background subtraction algorithms that analyze the temporal variability of pixel intensities, due to the complex texture and motion of the scene. They also present a challenge to segmentation algorithms that compare intensity or color distributions between the foreground and the background in each image independently, because ...

Background Subtraction via Robust Dictionary Learning

We propose a learning-based background subtraction approach based on the theory of sparse representation and dictionary learning. Our method makes the following two important assumptions: (1) the background of a scene has a sparse linear representation over a learned dictionary; (2) the foreground is “sparse” in the sense that majority pixels of the frame belong to the background. These two ass...

Background Subtraction in Videos using Bayesian Learning with Motion Information

This paper proposes an accurate and fast background subtraction technique for object tracking in still camera videos. Regions of motion in a frame are first estimated by comparing the current frame to a previous one. A samplingresampling based Bayesian learning technique is then used on the estimated regions to perform background subtraction and accurately determine the exact pixels which corre...

Background Subtraction with Dirichlet Processes

Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for further processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a perpixel measure of if a pixel belongs to the background or the foreground, whilst the regularisation brings in information from adj...

Background Subtraction with PTZ Cameras