Pedestrian action prediction using static image feature

In this study, we propose a method to predict how the pedestrian move (run or walk) in the future by using only appearance-based image features. Such kind of motion prediction significantly contributes to intelligent braking system in cars; knowing that the objects will run in several seconds such as for crossing streets, the car can start braking in advance, to effectively reduce the risk for crash accidents. In the proposed method, we empirically evaluate which frames preceding the target action, ‘running’ in this case, are effective for predicting it in the framework of feature selection. By using the most effective frames at which the image features are extracted, we can build the action prediction method. In the experiments, those frames are found around 0.37 second before running action and we also show that they are closely related to human motion phases from walking to running via biomechanical analysis.