No abstract

This work describes a two stages algorithm for camera motion analysis. First, it detects frames defining changes in the camera motion pattern, and then characterises the motion pattern for each video segment. The proposed scheme is motivated by hierarchical content-aware adaptation of on-line video. Its main characteristics are on-line operation, direct analysis on the compressed domain, efficiency and robustness. Results are similar to those achieved by state-of-the-art camera motion classification schemes based on iterative fitting to a motion model, despite involving a significantly smaller number of operations. *

No abstract

Estimation and compensation of the camera motion is the first step in many video analysis applications. Existing robust global motion estimation (GME) techniques have proven to tolerate reasonable amounts of outliers in the data. However, when these outliers convey the motion of large objects, GME remains a major challenge. This paper reviews the main causes that make GME with large objects particularly difficult. Then it proposes an iterative RANSAC-based approach that, by exploiting the properties of the different types of fits that can be found in the data, determines the most suitable scale a-posteriori and can recover the camera motion even when objects are dominant. Evaluation with synthetic and natural sequences demonstrates the good performance of our approach.

No abstract