Jorge Almeida Laser based tracking of mutually occluding dynamic objects University of Aveiro 2010 Department of Mechanical Engineering 10 September 2010

Objectives Motivation Laser Algorithm Experiments Results Conclusions O VERVIEW Overview

Develop an algorithm capable of following multiple targets –Overcoming temporary occlusions –Obtain position and velocity of targets Laser rangefinder Objectives O BJECTIVES

Obtain a dynamic perception of the vicinity Indoors –Building security –Optimization of motion paths Outdoors –Driver assistance systems –Advanced path planning Motivation I NTRODUCTION

2D Laser rangefinder Hokuyo UTM- 30 LX –30 m max range –40 Hz scan frequency –0.25° angular resolution –270° field of view Direct measurement of distance to targets Laser L ASER

Typical scan L ASER – S CAN Laser

Typical scan L ASER – S CAN Columns Wall Laser

Typical scan L ASER – S CAN Pedestrians Laser

Two main phases –Object reconstruction Preprocessing Segmentation Data reduction –Object association Motion prediction Tracking algorithm A LGORITHM

Remove noise Moving average filter –Applied to the data in polar coordinates (r, θ) The filter is limited in order not to compromise the responsiveness Obtain the Cartesian coordinates (x, y) Preprocessing O BJECT CREATION – P REPROCESSING

Clustering of measurements belonging to the same object Several steps –Occluded points detection –Clustering of visible and occluded points Euclidian distance between consecutive points Segmentation O BJECT CREATION – S EGMENTATION

Simplify the data handling Conversion from groups of points to lines –This representation is enough for all intended purposes Iterative End-Point Fit (IEPF) Data reduction O BJECT CREATION – D ATA REDUCTION

Search zones –Shaped as ellipses New objects are added to the tracking list Not associated objects are removed from the list Association aided by –Motion prediction –Heuristic rules Data association D ATA ASSOCIATION

Search zones –Shaped as ellipses New objects are added to the tracking list Not associated objects are removed from the list Association aided by –Motion prediction –Heuristic rules Data association D ATA ASSOCIATION

Centered at the object predicted position Aligned with the velocity vector Variable axes lengths –Object size –Occlusion time –Prediction errors Search zone D ATA ASSOCIATION – S EARCH ZONE

Adaptive linear Kalman filters –Two filters per object Constant velocity motion models Process noise covariance is coupled with the prediction error Motion prediction D ATA ASSOCIATION – M OTION PREDICTION

Increase performance Single associations Exclusion zones –ezA Prevents the tracking of objects’ fragments –ezB Avoids wrong associations Heuristic rules D ATA ASSOCIATION – H EURISTIC RULES

Robustness to occlusion in real world scenario –Outdoors people pathway –Global performance test Tracking of nearby moving objects –Person moving close to a wall –Security applications Experiments E XPERIMENTS

Long duration trial (~17 min) in a very crowded environment Ground-truth obtained with a video camera Performance evaluation –Percentage tracking time –Percentage of targets with tracking faults Loss of a target Id switch Fake tracks creation Real world scenario R ESULTS – R EAL WORLD SCENARIO

Real world scenario

Two distinct target types, single target (A) and multiple target (B) Good results Type B targets present worst results –Long occlusions Most common fault was target lost Real world scenario R ESULTS – R EAL WORLD SCENARIO TypeNumber of targets% time tracked% objects with tracking faults A B

Close proximity objects R ESULTS – C LOSE PROXUMITY OBJECTS

An algorithm capable of tracking multiple targets using laser data was developed. The algorithm was shown robust and effective even under extensive occlusion. The Kalman filter was an effective tool in the prediction of objects motion. Conclusions C ONCLUSIONS

Demonstration

Jorge Almeida Laser based tracking of mutually occluding dynamic objects University of Aveiro 2010 Department of Mechanical Engineering 10 September 2010