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AAAI
2008

Multimodal People Detection and Tracking in Crowded Scenes

9 years 9 months ago
Multimodal People Detection and Tracking in Crowded Scenes
This paper presents a novel people detection and tracking method based on a multi-modal sensor fusion approach that utilizes 2D laser range and camera data. The data points in the laser scans are clustered using a novel graph-based method and an SVM based version of the cascaded AdaBoost classifier is trained with a set of geometrical features of these clusters. In the detection phase, the classified laser data is projected into the camera image to define a region of interest for the vision-based people detector. This detector is a fast version of the Implicit Shape Model (ISM) that learns an appearance codebook of local SIFT descriptors from a set of hand-labeled images of pedestrians and uses them in a voting scheme to vote for centers of detected people. The extension consists in a fast and detailed analysis of the spatial distribution of voters per detected person. Each detected person is tracked using a greedy data association method and multiple Extended Kalman Filters that use ...
Luciano Spinello, Rudolph Triebel, Roland Siegwart
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where AAAI
Authors Luciano Spinello, Rudolph Triebel, Roland Siegwart
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