Visual motion estimation and tracking of rigid bodies by physical simulation

This thesis applies knowledge of the physical dynamics of objects to estimating object motion from vision when estimation from vision alone fails. It differentiates itself from existing physics-based vision by building in robustness to situations where existing visual estimation tends to fail: fast motion, blur, glare, distractors, and partial or full occlusion. A real-time physics simulator is incorporated into a stochastic framework by adding several different models of how noise is injected into the dynamics. Several different algorithms are proposed and experimentally validated on two problems: motion estimation and object tracking. The performance of visual motion estimation from colour histograms of a ball moving in two dimensions is improved considerably when a physics simulator is integrated into a MAP procedure involving non-linear optimisation and RANSAC-like methods. Process noise or initial condition noise in conjunction with a physics-based dynamics results in improved robustness on hard visual problems. A particle filter applied to the task of full 6D visual tracking of the pose an object being pushed by a robot in a table-top environment is improved on difficult visual problems by incorporating a simulator as a dynamics model and injecting noise as forces into the simulator.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

N-Body Spacetime Constraints

Animators frequently choreograph complex motions for multiple objects that interact through collision and obstruction. In such situations, the use of physically based dynamics to confer visual realism creates challenging computational problems. Typically forward simulation is well understood, but the inverse problem of motion synthesis---that of synthesizing motions consistent both with physical law and with the animator's requirements---is generally tedious and sometimes intractable. We show how N-body inverse problems can be formulated as optimization tasks. We present a simply stated, but combinatorially formidable example that exhibits all of the essential sources of complexity common to N-body motion synthesis, and show how it can be solved approximately using heuristic methods based on evolutionary computation. Key Words: Animation, motion synthesis, heuristic methods, stochastic optimization, evolutionary computation, billiard-ball problems. Introduction Simulating the dynamic..