Cited 3 time in webofscience Cited 2 time in scopus

Stereo-Based Tracking-by-Multiple Hypotheses Framework for Multiple Vehicle Detection and Tracking

Title
Stereo-Based Tracking-by-Multiple Hypotheses Framework for Multiple Vehicle Detection and Tracking
Authors
Lim, YC[Lim, Young-Chul]Kim, J[Kim, Jonghwan]Lee, CH[Lee, Chung-Hee]Lee, M[Lee, Minho]
DGIST Authors
Lim, YC[Lim, Young-Chul]; Kim, J[Kim, Jonghwan]; Lee, CH[Lee, Chung-Hee]
Issue Date
2013-07-17
Citation
International Journal of Advanced Robotic Systems, 10
Type
Article
Article Type
Article
Keywords
Accuracy and PrecisionBayesian FilterBayesian FiltersData AssociationMonte-Carlo MethodMultiple HypothesesMultiple HypothesisMultiple Object TrackingObstacle DetectionObstacle DetectorsSignal Filtering and PredictionSimilarity FunctionsStereo VisionTracking (Position)Vehicle Recognition
ISSN
1729-8814
Abstract
In this paper, we present a tracking-by-multiple hypotheses framework to detect and track multiple vehicles accurately and precisely. The tracking-bymultiple hypotheses framework consists of obstacle detection, vehicle recognition, visual tracking, global position tracking, data association and particle filtering. The multiple hypotheses are from obstacle detection, vehicle recognition and visual tracking. The obstacle detection detects all the obstacles on the road. The vehicle recognition classifies the detected obstacles as vehicles or non-vehicles. 3D feature-based visual tracking estimates the current target state using the previous target state. The multiple hypotheses should be linked to corresponding tracks to update the target state. The hierarchical data association method assigns multiple tracks to the correct hypotheses with multiple similarity functions. In the particle filter framework, the target state is updated using the Gaussian motion model and the observation model with associated multiple hypotheses. The experimental results demonstrate that the proposed method enhances the accuracy and precision of the region of interest. © 2013 Lim et al.
URI
http://hdl.handle.net/20.500.11750/2427
DOI
10.5772/56688
Publisher
SAGE Publications Ltd
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology1. Journal Articles
ETC1. Journal Articles


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