Background Subtraction
Gaussian Modeled Wronskian Technique for Background Subtraction
The novelty of this technique is to construct a stable background model from a given video sequence dynamically. The constructed background model is compared with different image frames of the same sequence to detect moving objects. In the proposed scheme the background model is constructed by analyzing a sequence of linearly dependent past image frames in Wronskian framework. The Wronskian based change detection model is further used to detect the changes between the constructed background scene and the considered target frame. The proposed scheme is an integration of Gaussian averaging and Wronskian change detection model. Gaussian averaging uses different modes which arise over time to capture the underlying richness of background, and it is an approach for background building by considering temporal modes. Similarly, Wronskian change detection model uses a spatial region of support in this regard. The proposed scheme relies on spatio-temporal modes arising over time to build the appropriate background model by considering both spatial and temporal modes.
Publication:
1. B. N. Subudhi, S. Ghosh and A. Ghosh, “Moving Object Detection using Gaussian Background Model and Wronskian Framework”, Accepted for publication in the Proceedings of 2nd International Symposium Pattern Recognition and Image Processing (PRIP-2013) (Published by IEEE), Mysore, India, 2013. (pdf)
2. B. N. Subudhi, S. Ghosh and A. Ghosh, "Change Detection for Moving Object Segmentation with Robust Background Construction under Wronskian Framework", Machine Vision and Applications, vol 24, no. 4, pp. 795-809, 2013. (pdf)
Statistical Feature Bags for Background Subtraction
This article proposes a novel background subtraction (BGS) technique to detect local changes corresponding to the movement of the objects in video scenes. Here we propose an efficient combination of six local features; three existing and three newly proposed. For background modeling and subtraction here a statistical parametric biunique model is proposed. In the proposed BGS scheme, during the background training phase, the multi-valued features corresponding to background pixels are collected. A few simple statistical parameters are used to characterize each feature. For background subtraction, the multi-valued features computed at each pixel location are compared with those of the computed parameters corresponding to that feature. For each pixel location, different labels (either object or background) are obtained due to different features. For assigning a final label to the pixel in the target frame a majority voting based label fusion technique is used. The proposed technique is successfully tested over several video sequences and found to be providing better results compared to various existing state-of-the-art techniques with three performance evaluation measures.
Publication:
B. N. Subudhi, S. Ghosh, S. C. K. Shiu, and A. Ghosh, "Statistical feature bag based background subtraction for local change detection", Information Sciences, (accepted). (IF: 4.038)