Paper Info
Title
Multiple Pedestrian Tracking Using Viterbi Data Association
Abstract
To address perception problems we must be able to track dynamic objects of the environment. An important issue of tracking is the association problem in which we have to associate each new observation with one existing object in the environment. This problem is complex: unfortunately, the number of observations generally does not correspond to the number of objects. Moreover, the number of objects is difficult to estimate since one object might be temporarily occluded or unobserved simply because objects can enter or go out of ranges of vehicle sensors. Moreover, the perception sensors or the object detection process might generate false alarm measurements. In this paper, we propose a new solution to solve the multiple objects tracking problem, using the Viterbi algorithm (VA) [2]. It is an established optimisation technique for discrete Markovian systems that has been extensively used in speech recognition. In this paper, we present an extension of VA to solve multiple objects tracking in clutter environment and show some experimental results on multiple pedestrian tracking and also some quantitative comparisons with MHT algorithms.
Year
DOI
Venue
2010
10.1109/IVS.2010.5548007
2010 IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV)
Keywords
Field
DocType
viterbi algorithm,markov processes,bayesian methods,vehicle dynamics,maximum likelihood estimation,speech recognition,layout,kalman filters,sensors,sensor fusion,hidden markov models
Computer vision,Object detection,Markov process,False alarm,Clutter,Computer science,Sensor fusion,Kalman filter,Artificial intelligence,Hidden Markov model,Viterbi algorithm
Conference
ISSN
Citations 
PageRank 
1931-0587
5
0.53
References 
Authors
1
2
Name
Order
Citations
PageRank
Asma Azim1361.87
Olivier Aycard230926.57