Laser based tracking of mutually occluding dynamic objects

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

Overview Overview • Objectives • Motivation • Laser • Algorithm • Experiments • Results • Conclusions

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

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

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

Laser –Scan Typical scan Laser

Laser –Scan Typical scan Wall Laser Columns

Laser –Scan Typical scan Laser Pedestrians

Algorithm Tracking algorithm • Two main phases • Object reconstruction • Preprocessing • Segmentation • Data reduction • Object association • Motion prediction

Object creation –Preprocessing Preprocessing • 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)

Object creation –Segmentation Segmentation • Clustering of measurements belonging to the same object • Several steps • Occluded points detection • Clustering of visible and occluded points • Euclidian distance betweenconsecutive points

Object creation –Data reduction Data reduction • 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 association Data association • Search zones • Shaped as ellipses • New objects are added to thetracking list • Not associated objects are removed from the list • Association aided by • Motion prediction • Heuristic rules

Data association – Search zone Search zone • Centered at the object predicted position • Aligned with the velocityvector • Variable axes lengths • Object size • Occlusion time • Prediction errors

Data association – Motion prediction Motion prediction • Adaptive linear Kalman filters • Two filters per object • Constant velocity motion models • Process noise covariance is coupled with the prediction error

Data association – Heuristic rules Heuristic rules • Increase performance • Single associations • Exclusion zones • ezA • Prevents the tracking of objects’ fragments • ezB • Avoids wrong associations

Experiments Experiments • 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

Results– Real world scenario Real world scenario • 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

Results– Real world scenario Real world scenario

Results– Real 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

Results – Close proxumity objects Close proximity objects

Conclusions Conclusions • 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.

Demonstration

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

Laser based tracking of mutually occluding dynamic objects