<title>A novel approach to object detection in video using region-based motion diffusion</title>

Abstract

Nowadays, the video coding standards for object based video coding and the tools for multimedia content description are available. Hence, we have powerful tools that can be used for content-based video coding, description, indexing and organization. In the past, it was difficult to extract higher level semantics, such as video objects, automatically. In this paper, we present a novel approach to moving object region detection. For this purpose, we developed a framework which applies bidirectional global motion estimation and compensation in order to identify potential foreground object regions. After spatial image segmentation, the results are assigned to image segments, and further diffused over the image region. This enables robust object region detection also in cases, where the investigated object does not move completely all the time. Finally, each image segment can be classified as being either situated in the foreground or in the background. Subsequent region merging delivers foreground object masks which can be used in order to define the region-of-attention for content based video coding, but also for contour based object classification.