Name
Abstract | ||
|---|---|---|
In this paper, we present an approach performing object behavior classification embedded in a complex and efficient perception method. This method, applied in dynamic outdoor environments using a moving vehicle equipped with a laser scanner, is composed of a local simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO). While the SLAM is performed by an implementation of incremental scan matching method, the tracking if performed by a Multiple Hypothesis Tracker (MHT) coupled with an adaptive Interacting Multiple Models Filter (IMM). The classification process takes place in the filtering stage and is based on one of the key parameters of the IMM filter which is the Transition Probability Matrix (TPM) modeling objects motion transitions. It permits to automatically classify object behavior and to reuse the classification output to enhance the prediction step in the filtering process. The experimental results on datasets collected from a Daimler Mercedes demonstrator in the framework of the European Project PReVENT-ProFusion2 demonstrate the capacity of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICARCV.2010.5707920 | Control Automation Robotics & Vision |
Keywords | Field | DocType |
SLAM (robots),driver information systems,object detection,object tracking,optical scanners,traffic engineering computing,Daimler Mercedes demonstrator,European Project PReVENT-ProFusion2,SLAM,adaptive interacting multiple models filter,dynamic outdoor environments,incremental scan matching method,laser scanner,moving object classification,moving object detection,moving object tracking,moving vehicle,multiple hypothesis tracker,multiple model interaction,object behavior classification embedded,simultaneous localization and mapping,transition probability matrix,DATMO,Object behavior classification,SLAM,TPM | Object detection,Computer vision,Laser scanning,Stochastic matrix,Computer science,Reuse,Filter (signal processing),Video tracking,Artificial intelligence,Simultaneous localization and mapping,Trajectory | Conference |
ISSN | ISBN | Citations |
2474-2953 | 978-1-4244-7814-9 | 1 |
PageRank | References | Authors |
0.45 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julien Burlet | 1 | 58 | 6.14 |
| Olivier Aycard | 2 | 309 | 26.57 |
| Qadeer Baig | 3 | 13 | 2.52 |