Abstract
For a contemporary intelligent transport system, congestion state analysis of traffic surveillance video (TSV) is one of the most crucial and intricate research topics because of the rapid development of transportation systems, the sustained growth of surveillance facilities on road, which lead to massive traffic flow data, and the inherent characteristics of our analysis target. Traditional methods on feature extractions are usually operated on Euclidean space in general, which are not accurate for high-dimensional TSV data analysis. This paper proposes a Grassmann manifold based neural network model to analysis TSV data , by mapping the video data from high dimensional Euclidean space to Grassmann manifold space, and considering the inner relation among adjacent cameras. The accuracy of the traffic congestion is improved, compared with several traditional methods. Experimental results are conducted to validate the accuracy of our method and to investigate the effects of different factors on performance.
Highlights
At present, state analysis of urban transportation is generally admitted as a progressively intricate issue due to the inherent complexity
In this paper we propose a neural network model based on Grassmann manifold
Since our method is related to Grassmann Sparse Representation (GSR) models, we select GSR based methods as baselines, which are listed below: Grassmann manifold Discriminant Analysis (GDA): A transform over the Grassmann manifold is learned to simultaneously maximize interclass distance and minimize intra-class distance in GDA
Summary
State analysis of urban transportation is generally admitted as a progressively intricate issue due to the inherent complexity. Urban transport system is considered intricate and massive. The quantity of deployed surveillance cameras are increasing in leaps and bounds globally. Under this circumstance, much more accurate state analysis of traffic surveillance video (TSV) is the vital problem demanding a prompt solution. Since the development of artificial intelligence and pattern recognition has risen steadily, the internal law of urban transportation, such as state analysis and estimate, is able to be digged out to a greater extent than before [1]. The intelligent transport system can be much more efficient, more collaborative and more predictive
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