01658303

Component

The network flow based traffic sign tracking models are widely used to process videos recorded by taxicabs to collect information, such as the locations and types, of traffic signs. One of the key questions in this type of tracking models is the feature selection problem, which determines the features and their associated weights to construct the costs on the edges of the underlying network. However, in existing studies, the features and their weights are set mostly by intuition and there are no studies that systematically investigate this problem. The authors investigate the feature selection problem by considering a wide range of features related to traffic sign candidates, including its position, size, frame index, color histogram, SURF descriptors, and template matching similarities. They propose to use the multinomial logit model to identify features that have significant influence on the edge costs and to estimate their corresponding weights. The authors test the performance of their selected features using real videos recorded by taxicabs in the city of Beijing. They find that the position and frame index of the traffic sign candidates are the top two features that influence the tracking results. The authors also find that image similarity measures are helpful features, but their contribution is not as large as the previous two features.

This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems. Alternate title: On Feature Selection in Network Flow–Based Traffic Sign Tracking Models

18-00669

English

9p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; Photos; References; Tables

Automatic tracking; Detection and identification system applications; Detection and identification systems; Image analysis; Image processing; Logits; Networks; Taxicabs; Traffic flow; Traffic signs; Video imaging detectors

Beijing (China)

Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment

Transportation Research Board Annual Meeting 2018 Paper #18-00669

TRIS, TRB, ATRI

Jan 8 2018 10:10AM