A LVQ-Based Temporal Tracking for Semi-Automatic Video Object Segmentation

Abstract

This paper presents a Learning Vector Quantization (LVQ)-based temporal tracking method for semi-automatic video object segmentation. A semantic video object is initialized using user assistance in a reference frame to give initial classification of video object and its background regions. The LVQ training approximates video object and background classification and use them for automatic segmentation of the video object on the following frames thus performing temporal tracking. For LVQ training input, we sampling each pixel of a video frame as a 5-dimensional vector combining 2-dimensional pixel position (X,Y) and 3-dimensional HSV color space. This paper also demonstrates experiments using some MPEG-4 standard test video sequences to evaluate the accuracy of the proposed method.