http://www.idiap.ch/~odobez/publications/OdobezBouthemy-IJVCIR1995.pdf

Robust multiresolution estimation of parametric motion models

Standard approaches to motion analysis assume that the optic flow is smooth; such techniques have trouble dealing with occlusion boundaries. The image sequence can be decomposed into a set of overlapping layers, where each layer's motion is described by a smooth flow field. The discontinuities in the description are then attributed to object opacities rather than to the flow itself, mirroring the structure of the scene. A set of techniques is devised for segmenting images into coherently moving regions using affine motion analysis and clustering techniques. It is possible to decompose an image into a set of layers along with information about occlusion and depth ordering. The techniques are applied to a flower garden sequence. The scene can be analyzed into four layers, and, the entire 30-frame sequence can be represented with a single image of each layer, along with associated motion parameters. >

Layered representation for motion analysis

The goal of this paper is to offer a structured synopsis of the problems in image motion computation and analysis, and of the methods proposed, exposing the underlying models and supporting assumptions. A sufficient number of pointers to the literature will be given, concentrating mostly on recent contributions. Emphasis will be on the detection, measurement and segmentation of image motion. Tracking, and deformable motion issues will be also addressed. Finally, a number of related questions which could require more investigations will be presented.

Computation and analysis of image motion: a synopsis of current problems and methods

This article deals with analysis of the dynamic content of a scene from an image sequence irrespective of the static or dynamic nature of the camera. The tasks involved can be the detection of moving objects in a scene observed by a mobile camera, or the identification of the movements of some relevant components of the scene relatively to the camera. This problem basically requires a motion-based segmentation step. We present a motion-based segmentation method relying on 2-D affine motion models and a statistical regularization approach which ensures stable motion-based partitions. This can be done without the explicit estimation of optic flow fields. Besides these partitions are linked in time. Therefore, the motion interpretation process can be performed on more than two successive frames. The ability to follow a given coherently moving region within an interval of several images of the sequence makes the interpretation process more robust and more comprehensive. Identification of the kinematic components of the scene is induced from an intermediate layer accomplishing a generic qualitative motion labeling. No 3-D measurements are required. Results obtained on several real-image sequences corresponding to complex outdoor situations are reported.

Motion segmentation and qualitative dynamic scene analysis from an image sequence

This contribution investigates local differential techniques for estimating optical flow and its derivatives based on the brightness change constraint. By using the tensor calculus representation we build the Taylor expansion of the gray-value derivatives as well as of the optical flow in a spatiotemporal neighborhood. Such a formulation simplifies a unifying framework for all existing local differential approaches and allows to derive new systems of equations to estimate the optical flow and its derivatives. We also tested various optical flow estimation approaches on real image sequences recorded by a calibrated camera fixed on the arm of a robot. By moving the arm of the robot along a precisely defined trajectory we can determine the true displacement rate of scene surface elements projected into the image plane and compare it quantitatively with the results of different optical flow estimators.

/pdf/optical-flow-estimation-advances-and-comparisons-1vj1ghktxe.pdf

Optical flow estimation: advances and comparisons

A closed-form solution for motion estimation from first-order flow in two 'distinct' image regions is described. Uniqueness is guaranteed when these correspond to surface patches with different normal vectors. given an image sequence, the authors show how the image many be segmented into regions with the necessary properties, optical flow is computed for these regions, and motion parameters are computed. The method can be applied to arbitrary scenes and camera motion. The authors explain why it is more robust than other techniques that require the knowledge of the full flow or information up to the second-order terms of it. Experimental results are presented to support the theoretical derivations. >

S. Negahdaripour

Papers

Motion recovery from image sequences using First-order optical flow information