A Segmentation Method of Objects Based on Truncated Cone BLOB

Segmentation of objects in real-world scenes needs to detect and eliminate noise such as shadow,reflection and ghost. The detection of static visual object is also crucial for robust segmentation and dynamic background updating. To realize systemic object segmentation for real-world scenes,the paper proposed a new adaptive method based on truncated cone BLOB. Firstly,based on BLOB knowledge,the method transferred 3D color space to 2D angle-mode color space and defined a set of rules of truncated cone to detect shadow BLOB and reflection BLOB. Secondly,the method removed the ghost BLOB from the moving visual BLOB by calculating permanence memory. Finally,the method used a long-time and short-time dual-background model to detect static visual object. The experimental results on different scenes demonstrate the effectiveness of the proposed method. In addition,the proposed method can be used for different tracking techniques.