18300844. SPATIAL ACTION LOCALIZATION IN THE FUTURE (SALF) simplified abstract (HONDA MOTOR CO., LTD.)
Contents
- 1 SPATIAL ACTION LOCALIZATION IN THE FUTURE (SALF)
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SPATIAL ACTION LOCALIZATION IN THE FUTURE (SALF) - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SPATIAL ACTION LOCALIZATION IN THE FUTURE (SALF)
Organization Name
Inventor(s)
Hyung-gun Chi of West Lafayette IN (US)
Kwonjoon Lee of San Jose CA (US)
Nakul Agarwal of San Francisco CA (US)
Chiho Choi of San Jose CA (US)
SPATIAL ACTION LOCALIZATION IN THE FUTURE (SALF) - A simplified explanation of the abstract
This abstract first appeared for US patent application 18300844 titled 'SPATIAL ACTION LOCALIZATION IN THE FUTURE (SALF)
Simplified Explanation
The patent application describes a method for spatial action localization in the future (SALF) using a combination of encoder, future feature predictor, and decoder to predict future actions in a video clip.
- The method involves feeding a frame from a video clip through an encoder to generate a latent feature.
- The latent feature, along with latent features from previous time steps, is fed through a future feature predictor to generate cumulative information for the time step.
- The cumulative information is then passed through a decoder to generate a predicted action area and classification.
- An action is implemented based on the predicted action area and classification.
Potential Applications
This technology could be applied in video surveillance systems, autonomous vehicles, and human-computer interaction for predicting future actions based on past behavior.
Problems Solved
This technology solves the problem of accurately predicting future actions in a video sequence, which can be useful for various applications such as security monitoring and behavior analysis.
Benefits
The benefits of this technology include improved accuracy in predicting future actions, which can lead to better decision-making in real-time scenarios and enhanced overall system performance.
Potential Commercial Applications
- "Predictive Action Localization Technology for Enhanced Video Surveillance Systems"
Possible Prior Art
There may be prior art related to video prediction algorithms and action recognition systems that could be relevant to this technology.
Unanswered Questions
How does this technology compare to existing video prediction methods in terms of accuracy and efficiency?
This article does not provide a direct comparison with existing video prediction methods, so it is unclear how this technology performs in relation to others.
What are the potential limitations or challenges in implementing this technology in real-world applications?
The article does not address any potential limitations or challenges that may arise when implementing this technology in practical scenarios.
Original Abstract Submitted
According to one aspect, spatial action localization in the future (SALF) may include feeding a frame from a time step of a video clip through an encoder to generate a latent feature, feeding the latent feature and one or more latent features from one or more previous time steps of the video clip through a future feature predictor to generate a cumulative information for the time step, feeding the cumulative information through a decoder to generate a predicted action area and a predicted action classification associated with the predicted action area, and implementing an action based on the predicted action area and the predicted action classification. The encoder may include a 2D convolutional neural network (CNN) and/or a 3D-CNN. The future feature predictor may be based on an ordinary differential equation (ODE) function.
