Video Object Extraction for Object-Oriented Applications

Changick Kim, Jenq-Neng Hwang

Abstract: In this paper, we propose two novel video object (VO) extraction schemes, specifically designed for two different scenarios of content-based video analysis applications. One is a change detection-based VO extraction algorithm appropriate to surveillance type video sequences, where automatic detection of new appearance of objects are important in envisaging on-line object-oriented applications as well as object-based coding. The other is an object tracking-based method, which is especially robust to video sequences with moving background, although human intervention is needed in the process. In both cases, the semantically meaningful video objects are obtained by a final regularization stage realized by means of a cascade of morphological filters. Experimental results obtained on the MPEG-4 test sequences are presented respectively.