Object identification, enhancement and tracking under dynamic background conditions

A real-time event tracking method is proposed that is immune to background variances. The proposed method models each pixel as a collection of Gaussian distributions to handle background variations and uses manipulations in the RGB space to mitigate the effects of foreground shadows. A two stepped connected component analysis method is also introduced in refining the estimated foreground and clustering pixels into silhouettes based on objects. Pixel clusters are formed by filling inter-cluster pixels on the basis of neighborhood solidity of individual pixels. Clustered pixels are defined as object fragments and objects are formed by combining object fragments considering their size and mutual distances. The proposed tracker employs an algorithm to boil down the multiple object interaction problems (objects merging, objects splitting, new object appearance and lost objects) into a simple matrix interpretation problem to construct a consistent feature space. Specifically, splitting of merged objects and temporary disappearances of objects due to occlusions with background objects are handled by the means of a feature correspondence matching. A novel object identification method is proposed for this purpose.