TY - BOOK ID - 700737 TI - Moving Object Detection Using Background Subtraction AU - Shaikh, Soharab Hossain. AU - Saeed, Khalid. AU - Chaki, Nabendu. PY - 2014 SN - 3319073869 3319073850 1322136599 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Computer vision KW - Context-aware computing KW - Pattern recognition systems KW - Methodology. KW - Data processing. KW - Pattern classification systems KW - Pattern recognition computers KW - Pattern perception KW - Context-aware pervasive systems KW - Context-awareness (Computer science) KW - Ubiquitous computing KW - Global system for mobile communications KW - Mobile computing KW - Sensor networks KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Image processing KW - Computer vision. KW - Multimedia systems. KW - Computer Imaging, Vision, Pattern Recognition and Graphics. KW - Image Processing and Computer Vision. KW - Multimedia Information Systems. KW - Computer-based multimedia information systems KW - Multimedia computing KW - Multimedia information systems KW - Multimedia knowledge systems KW - Information storage and retrieval systems KW - Optical data processing. KW - Multimedia information systems. KW - Optical computing KW - Visual data processing KW - Bionics KW - Electronic data processing KW - Integrated optics KW - Photonics KW - Computers KW - Optical equipment UR - https://www.unicat.be/uniCat?func=search&query=sysid:700737 AB - This Springer Brief presents a comprehensive survey of the existing methodologies of background subtraction methods. It presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes. This well-known methodology has applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition. The authors identify common challenges faced by researchers including gradual or sudden illumination change, dynamic backgrounds and shadow and ghost regions. This brief concludes with predictions on the future scope of the methods. Clear and concise, this brief equips readers to determine the most effective background subtraction method for a particular project. It is a useful resource for professionals and researchers working in this field. ER -