Authors:
Kenji Nishida
1
;
Takumi Kobayashi
1
;
Taro Iwamoto
2
and
Shinya Yamasaki
1
Affiliations:
1
National Institute of Advanced Industrial Science and Technology (AIST), Japan
;
2
Mazda Motor Co., Japan
Keyword(s):
Action Prediction, Feature Selection, Intelligent Transport System, Image Feature Extraction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computer-Supported Education
;
Data Manipulation
;
Domain Applications and Case Studies
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image Processing and Artificial Vision Applications
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
In this study, we propose a method to predict how the target object 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; by knowing that the objects will run in several seconds
such as in crossing streets, the car can start to brake in advance, which effectively reduces 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 from the
viewpoint of biomechanics.