Looking at people and Image-based Localisation

Looking at people and

Image-based LocalisationRoberto Cipolla

Department of Engineering

Research team http://www.eng.cam.ac.uk/~cipolla/people.html

1. Real-time hand detection and tracking

Why is it hard?

• Highly articulated object, 27 model parameters

• Shape variation and self-occlusions

• Unreliable point features

• Ambiguities in single viewlead to multi-modal distributions (local minima)

Why is it hard?

• Background clutter

• Potentially fast motion

• Lighting changes

• Partial / full occlusion

A Solved Problem?

3D tracking, 6/7 DOF• Model: 3D quadrics• Cost Function:

Edges or colour-edges • Tracking: Unscented

Kalman filtering• Single or dual view• Single hypothesis

filter, no recovery strategy

A Robust Tracker

• Should work in scenes with complex backgroundand varying illumination– Important: Cost function design– Optimization strategy

• Should handle multi-modality– Examples: Particle filters, multi-hypotheses filters

• Should have a recovery strategy when track is lost– Trigger search algorithm

3D Pose Recovery

3D hand model constructed from cones and ellipsoids Contour projection, handling self-occlusions 27 motion parameters

Hierarchy of classifiers

Likelihood : Edges

Edge Detection Projected Contours

Robust EdgeMatching

Input Image 3D Model

Chamfer Matching

Input image Canny edges

Distance transform Projected Contours

Likelihood : Colour

Skin Colour ModelProjected Silhouette

Input Image 3D Model

Template Matching

Tree-based bayesian filtering

Matching Multiple Templates

Use tree structure to efficiently match many templates (>50,000) Arrange templates in tree based on their similarity Traverse tree using breadth-first search, several ‘active’ leaves possible

Search TreeGrid-based partitioning of parameter space

Bayesian-Tree

• The search-tree is brought into a Bayesian framework by adding the prior knowledge from previous frame.

• The Bayesian-Tree can be thought as approximating the posterior probability at different resolutions.

State space partitioning Estimation of posterior pdf

Experiments

Global Motion3D motions limited to hemisphere

Dynamics: First-order Gaussian process

3 level tree with 16,000 templates at leaf level

5 scales, divisions of 15 degrees in 3D rotation and

divisions of 10 degrees in image plane rotation

Translation search at 20, 5, 2-pixel resolution

Tracking Results

Tracking Results

Experiments

Finger Articulation

• Opening and closing of thumb and fingers approximated by 2 parameters

• Global motion restricted to smaller range, but still with 6 DOF

• 35,000 templates at the leaf level

Opening and closing

Hand detection system

Ongoing work

Large number of templates requiredExamples shown here show only constrained motionNumber of templates required for fully articulated motion?

Tracking rates at 5 fps to 0.2 fps For 400 - 35,000 templates (on a 2.4 GHz Pentium IV)

Error introduced by geometric model No palm deformation, no skin deformation, no arm model

Detecting people

2. Building 3D models of cities

Trumpington Street Data

Camera pose determination

3D reconstruction

Reconstruction texture mapped

3. Where am I?

Image-based localisation

......

Image-based localisation

Image-based localisation

……

Image-based localisation

Image-based localisation

Image-based localisation

Image-based localisation

Image-based localisation

Image-based localisation

Summary and deliverables

• Realtime hand detection in clutter

• 3D models from uncalibrated images

• Image-based localisation for augmented reality