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Structured behaviour prediction of on-road vehicles via deep forest

Structured behaviour prediction of on-road vehicles via deep forest

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Vision-based vehicle behaviour analysis has drawn increasing research efforts as an interesting and challenging issue in recent years. Although a variety of approaches have been taken to characterise on-road behaviour, there still lacks a general model for interpreting the behaviour of vehicles on the road. In this Letter, the authors propose a new method that effectively predicts the vehicle behaviour based on structured deep forest modelling. Inspired by structured learning, the structure information of vehicle behaviour is extracted from the detected vehicle, and then the corresponding structured label is constructed. Especially, the structured label visually expresses the vehicle behaviour as contrast to the discrete numerical label. With the structured label, a structured deep forest model is proposed to predict the vehicle behaviour. Experimental results illustrate that the proposed method successfully obtains the implication of semantic interpretation of vehicle behaviour by the predicted structured labels, and meanwhile it achieves comparable performance with traditional methods.

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