An improved scheme for multifeature-based foreground detection using challenging conditions

Abstract

Detecting an accurate foreground from a video frame is a critical task under different complex situations such as sudden changes in illuminations, relocation of background objects, shadow, low contrast videos and dynamic backgrounds (like waving tree, rippling of water etc.). Most of the existing schemes utilize a single feature-based foreground detection approach, which in turn is hard to apply under aforementioned complex situations. In order to mitigate this issue and properly exploit the different characteristics of the pixels, the present work proposes an efficient foreground detection scheme for better segmenting the foreground. In the proposed scheme, first the texture features are first extracted utilizing cross-diagonal texture matrix (CDTM), which essentially combines the merits of both the gray level co-occurrence matrix (GLCM) and the texture spectrum (TS) to provide a complete texture information about a frame. The color and gray value features of the pixel along with texture features are utilized for the feature vector generation. Second, during background modeling phase, the similarity distance measure is computed employing the Canberra distance between the mean feature vector of the current frame and the model. Finally, a method for adaptively selecting the threshold value is proposed, instead of choosing heuristically to correctly classify the foreground and background pixels under the dynamic background condition when background pixels changing frequently.Experiments are conducted using a wide variety of indoor and outdoor video sequences under various different challenging conditions and the results compared with that of the existing state-of-the-art methods. From the experimental results, it is shown that the proposed scheme outperforms the existing schemes in terms of the quantitative as well as qualitative measures.