Preindication Mining for Predicting Pedestrian.Action Change
Preindication Mining for Predicting Pedestrian.Action Change
复制标题
用于预测行人动作变化的预指示挖掘
DOI：
10.1007/978-3-319-48506-5_18
复制
发表时间：
2016
影响因子：
2.8
通讯作者：
T.Iwamoto. S.Yamasaki
中科院分区：
文献类型：
--
作者：
K.Nishida;T.Kobayashi;T.Iwamoto. S.Yamasaki
行人的行为预测对汽车智能制动系统有很大贡献，知道行人会在几秒钟内奔跑（例如过马路），汽车可以提前开始制动，有效降低碰撞事故的风险。在本文中，我们提出了一种方法来预测行人的行为（运行或步行）在未来的基础上，在视频帧中检测到的外观为基础的图像特征的预指示。我们凭经验挖掘的独特的帧之前的目标行动，“运行”在这种情况下，并有效地预测它的框架中的特征选择。通过使用最有效的帧，我们可以通过利用在这些帧中提取的图像特征来构建动作预测方法。在图像特征提取方法方面，本文对GLAC（梯度局部自相关）和HOG（方向梯度直方图）两类特征进行了评价。在实验中，使用GLAC特征成功地找到了有效帧运行动作前0.37 s左右，这是不是HOG的情况。我们还表明，结果是密切相关的人体运动阶段，从步行到跑步，通过生物力学分析。
The action prediction of pedestrians significantly contributes to an intelligent braking system in cars; knowing that the pedestrians will run in several seconds such as for crossing streets, the cars can start braking in advance, to effectively reduce the risk for crash accidents. In this paper, we propose a method to predict how the pedestrian act (run or walk) in the future based on preindication in video frames detected by only appearance-based image features. We empirically mine the distinctive frames that precede the target action, ‘running’ in this case, and are effective for predicting it in the framework of feature selection. By using the most effective frames, we can build the action prediction method by exploiting the image features extracted at those frames. As to the image feature extraction methods, we evaluate two types of features in our method, one is GLAC (Gradient Local AutoCorreration) and the other is HOG (Histogram of Oriented Gradient). In the experiments, the effective frames are successfully found around 0.37 s before running action by using GLAC feature; this is not the case of HOG. We also show that the results are closely related to human motion phases from walking to running via biomechanical analysis.