Open AccessOn‐road multi‐vehicle tracking algorithm based on an improved particle filter
Author(s) -
Liu Peixun,
Li Wenhui,
Wang Ying,
Ni Hongyin
Publication year - 2015
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2014.0088
Subject(s) - vehicle tracking system , tracking (education) , particle filter , computer science , vehicle dynamics , collision avoidance , tracking system , artificial intelligence , computer vision , algorithm , filter (signal processing) , engineering , simulation , real time computing , collision , kalman filter , automotive engineering , psychology , pedagogy , computer security
Forward collision avoidance systems have shown to be a particularly effective crash‐avoidance technology. Multi‐vehicle tracking capabilities play an important role in the real‐world performance and effectiveness of such systems. In order to effectively and accurately track vehicles in a moving platform and in complicated road environments, the authors proposed a multi‐vehicle tracking algorithm based on an improved particle filter. First, the authors used a vehicle disappearance detection and handling mechanism based on the normalised area of the minimum circumscribed rectangle of particle distributions. This mechanism is used to verify whether a new target is a vehicle and can also handle the vehicle exit during the tracking phase. Next, an improved particle filter‐based framework, which includes a new process dynamical distribution, allowed for multi‐vehicle tracking capabilities was used for vehicle tracking. Finally, an effective occlusion detection and handling mechanism was used to address the significant occlusion between vehicles. The combination of these added improvements in the algorithm results in the enhancement of the vehicle tracking rate in a variety of challenging conditions. Experimental tests carried out from different datasets show excellent performance in multi‐vehicle tracking, in terms of accuracy in complex traffic situations and under different lighting conditions.