Tracking and Detection of Lane and Vehicle Integrating Lane and Vehicle Information Using Probabilistic Data Association Filter
Date Issued
2009
Date
2009
Author(s)
Hung, Ssu-Ying
Abstract
We propose a robust system for multi-vehicle and multi-lane detection with integrating lane and vehicle information. Most research work only can detect the lanes or vehicles separately. However, the dependency between lane information and vehicle information are able to support each other achieving more reliable results. In probabilistic data association filter (PDAF) track model, cumulate history of target is keep in the data association probability and the weight of each detected features are estimated to be the likelihood; therefore we use probabilistic data association filter to integrate information of lane and vehicle. The core of our method is to combine the lanes and vehicles by improve the data association probability with observational relation.or lane detection and tracking, tracked vehicles provide the orientation and position information. The tracked lane also gives the direction and boundary to the vehicle detection and tracking. According to the lane information, we can know where is impossible to appear the moving vehicles. Experimental results show that our approach can detect multi-vehicle and multi-lane more reliably
Subjects
vehicle detection
lane detection
Type
thesis
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