Structured behaviour prediction of on-road vehicles via deep forest
Structured behaviour prediction of on-road vehicles via deep forest
复制标题
通过深层森林进行道路车辆的结构化行为预测
DOI：
10.1049/el.2019.0472
复制
发表时间：
2019
影响因子：
1.1
通讯作者：
Chen Yanyan
中科院分区：
文献类型：
--
作者：
Mou Luntian;Mao Shasha;Xie Haitao;Chen Yanyan
近年来，基于视觉的车辆行为分析作为一个有趣且具有挑战性的问题吸引了越来越多的研究工作。虽然已经采取了各种方法来描述道路行为，但仍然缺乏一个通用模型来解释车辆在道路上的行为。在这封信中，作者提出了一种新的方法，可以有效地预测基于结构化深森林模型的车辆行为。受结构化学习的启发，从检测到的车辆中提取车辆行为的结构信息，然后构造相应的结构化标签。特别地，结构化标签在视觉上表达了车辆行为，与离散数字标签形成对比。利用结构化标签，提出了一种结构化深森林模型来预测车辆行为。实验结果表明，该方法通过预测的结构化标签成功地获得了车辆行为的语义解释含义，同时取得了与传统方法相当的性能。
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.