An Adaptive Traffic Flow Analysis Scheme Based on Scene-Specific Sample Collection and Training
碩士 === 國立中央大學 === 資訊工程研究所 === 99 === This research presents a framework of analyzing the traffic information in the surveillance videos from the static roadside cameras to assist solving the vehicle occlusion problem for more accurate traffic flow estimation and vehicle classification. The proposed...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2011
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| Online Access: | http://ndltd.ncl.edu.tw/handle/90511464225852245271 |
| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 99 === This research presents a framework of analyzing the traffic information in the surveillance videos from the static roadside cameras to assist solving 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 from the characteristics of masks. Their statistics are employed to automatically establish the models of scene, including the implicit shape model of vehicles and the support vector machine of feature points. 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 and support vector machine to recognize vehicles. Each feature point is classified into a vehicle type and processed by the corresponding ISM. Experimental results demonstrate that the proposed scheme can deal with the scenes with different characteristics in the traffic surveillance videos.
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