Adaptive Traffic Scene Analysis by using Implicit Shape Model

• Main Authors: Kai-kai Hsu, 許凱凱
• Other Authors: Po-Chyi Su
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/45092056055145764219

碩士 === 國立中央大學 === 資訊工程研究所 === 98 === This research presents a framework of analyzing the traffic information in the surveillance videos from the static roadside cameras to assist resolving the vehicle occlusion problem for more accurate traffic flow estimation and vehicle classification. The proposed scheme consists of two main parts. The first part is a model training mechanism, in which the traffic and vehicle information will be collected and their statistics are employed to automatically establish the model of the scene and the implicit shape model of vehicles. It should be noted that the proposed self-training mechanism can reduce a great deal of human efforts. The second part adopts the established implicit shape model, which is a highly flexible learned representation, for vehicle recognition when possible occlusions of vehicles are detected. Experimental results demonstrate that the proposed scheme can deal with the scenes with different characteristics and the occlusion problem in traffic surveillance videos can be reasonably resolved.