The 2010 IEEE/RSJ International Conference on Intelligent RObots and Systems, Taipei International Convention Center, Taipei, Taiwan, October 18-22, 2010

IROS 2010 Paper Abstract


Paper TuET6.2

Yuan, Chunrong (Eberhard Karls University of Tübingen), Schwab, Isabell (University of Tübingen), Recktenwald, Fabian (University of Tübingen), Mallot, Hanspeter (Eberhard Karls University of Tübingen)

Detection of Moving Objects by Statistical Motion Analysis

Scheduled for presentation during the Regular Sessions "Computer Vision II" (TuET6), Tuesday, October 19, 2010, 17:00−17:20, 201A

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 18-22, 2010, Taipei International Convention Center, Taipei, Taiwan

This information is tentative and subject to change. Compiled on October 16, 2021

Keywords Computer Vision, Navigation, Intelligent Vehicles


In this work we present a new approach for the detection of moving objects observed by a mobile camera, which is a critical issue related to autonomous robot navigation as well as driver/pilot assistance systems. In order to separate individual object motions from the self-motion of the observing camera, we implement a linear method to recover the full set of 3D motion parameters undergone by the camera. Based on the recovered camera motion and reconstructed depth information of the detected scene points, a criterion has been derived to determine how well individual scene points agree with the estimated camera motion. The classification of scene points is achieved by statistical analysis of the probability distribution function of the points' motion characteristics. After the initial classification, the identified dynamic scene points are further clustered into different objects by taking into account the underlying geometric distribution in the image. The approach is unique in that it can detect moving objects using a single pair of images and is completely automated. Several experiments have been carried out in challenging environments using two different hardware setups. A comparative study shows that the proposed classification method generates fewer false alarms compared to a standard one.



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