Particle-filter-based Vehicle Tracking within the Urban Intersection in Real-Time

• Main Authors: Wu, Cheng En, 吳承恩
• Other Authors: Wang, Jia Shung
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/56704468968650319921

碩士 === 國立清華大學 === 資訊工程學系 === 104 === Intelligent transportation system is an important target to urban development vision for a smart city. Therefore, this thesis is focus on gathering traffic information within the urban intersection where accidents frequently occur. Nowadays, most of urban intersections are being installed surveillance cameras so as to exploit and explore these vision-based contents, such as deploying the vehicle tracker to locate and record the trajectories instantaneously. In this thesis, a real-time vehicle tracker within the urban intersection is proposed. The tracking method is based on the concept of particle-filter and coupled with the Hidden Markov model (HMM), which provides the capabilities of trajectory classification and tracklet prediction. For tracking all of trajectories in real-time is a computational challenge, on the basis of collaborating previous records of vehicle movement and future tracklet prediction given by HMM. The proposed method removes most of the particles. Moreover, several tips for effectively implementing are included, such as (1) utilizing already-fixed trajectories (surrounding vehicles) to boost vehicle tracking accuracy; (2) vehicle ID labeling to identify relationships between surrounding vehicles. The experimental results demonstrate both the computational effectiveness and tracking correctness of the proposed method, the tracker truly execute in real-time for the intersections of six traffic lanes , say around six vehicles per second on tracking.