Future-Frame Prediction for Fast-Moving Objects with Motion Blur

We propose a deep neural network model that recognizes the position and velocity of a fast-moving object in a video sequence and predicts the object’s future motion. When filming a fast-moving subject using a regular camera rather than a super-high-speed camera, there is often severe motion blur, ma...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-08, Vol.20 (16), p.4394
Main Authors: Lee, Dohae, Oh, Young Jin, Lee, In-Kwon
Format: Article
Language:English
Subjects:
Artificial intelligence
Blurring
Cameras
Frames (data processing)
future frame prediction
High speed cameras
machine physical reasoning
Methods
motion blur
Neural networks
Object motion
Physical properties
Physics
Prediction models
Principal components analysis
Simulation
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Summary:We propose a deep neural network model that recognizes the position and velocity of a fast-moving object in a video sequence and predicts the object’s future motion. When filming a fast-moving subject using a regular camera rather than a super-high-speed camera, there is often severe motion blur, making it difficult to recognize the exact location and speed of the object in the video. Additionally, because the fast moving object usually moves rapidly out of the camera’s field of view, the number of captured frames used as input for future-motion predictions should be minimized. Our model can capture a short video sequence of two frames with a high-speed moving object as input, use motion blur as additional information to recognize the position and velocity of the object, and predict the video frame containing the future motion of the object. Experiments show that our model has significantly better performance than existing future-frame prediction models in determining the future position and velocity of an object in two physical scenarios where a fast-moving two-dimensional object appears.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20164394