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Paper WeC09.2

Kaestner, Ralf (ETH Zurich), Maye, Jerome (ETH Zurich), Pilat, Yves (ETH Zurich), Siegwart, Roland (ETH Zurich)

Generative Object Detection and Tracking in 3D Range Data

Scheduled for presentation during the Regular Session "Visual Tracking" (WeC09), Wednesday, May 16, 2012, 14:45−15:00, Meeting Room 9 (Sa)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 24, 2017

Keywords Range Sensing, Human Detection & Tracking

Abstract

This paper presents a novel approach to tracking dynamic objects in 3D range data. Its key contribution lies in the generative object detection algorithm which allows the tracker to robustly extract objects of varying sizes and shapes from the observations. In contrast to tracking methods using discriminative detectors, we are thus able to generalize over a wide range of object classes matching our assumptions. Whilst the generative model underlying our framework inherently scales with the complexity and the noise characteristics of the environment, all parameters involved in the detection process obey a clean probabilistic interpretation. Nevertheless, our unsupervised object detection and tracking algorithm achieves real-time performance, even in highly dynamic scenarios covering a significant amount of moving objects. Through an application to populated urban settings, we are able to show that the tracking performance of the presented approach yields results which are comparable to state-of-the-art discriminative methods.

 

 

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