Nec corporation (20240135751). ACTION RECOGNITION APPARATUS, TRAINING APPARATUS, ACTION RECOGNITION METHOD, TRAINING METHOD, AND STORAGE MEDIUM simplified abstract
Contents
- 1 ACTION RECOGNITION APPARATUS, TRAINING APPARATUS, ACTION RECOGNITION METHOD, TRAINING METHOD, AND STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ACTION RECOGNITION APPARATUS, TRAINING APPARATUS, ACTION RECOGNITION METHOD, TRAINING METHOD, AND STORAGE MEDIUM - 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
ACTION RECOGNITION APPARATUS, TRAINING APPARATUS, ACTION RECOGNITION METHOD, TRAINING METHOD, AND STORAGE MEDIUM
Organization Name
Inventor(s)
Yasunori Babazaki of Tokyo (JP)
ACTION RECOGNITION APPARATUS, TRAINING APPARATUS, ACTION RECOGNITION METHOD, TRAINING METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135751 titled 'ACTION RECOGNITION APPARATUS, TRAINING APPARATUS, ACTION RECOGNITION METHOD, TRAINING METHOD, AND STORAGE MEDIUM
Simplified Explanation
The patent application describes a technique for accurately recognizing an action of a person in an image sequence, even when the region related to the action is not fully shown.
- First calculation section calculates action feature information from a partial sequence.
- Second calculation section corrects the action feature information using past feature information.
- Action recognition section recognizes the action based on the corrected information.
Potential Applications
This technology could be applied in security systems, surveillance cameras, sports analytics, and human-computer interaction systems.
Problems Solved
This technology solves the problem of accurately recognizing actions in image sequences where the relevant region may not be fully visible.
Benefits
The benefits of this technology include improved accuracy in action recognition, enhanced security monitoring, and better analysis of human movements.
Potential Commercial Applications
Potential commercial applications include security systems for public spaces, sports training and analysis software, and interactive gaming systems.
Possible Prior Art
Prior art in the field of action recognition includes various techniques using machine learning algorithms, motion sensors, and computer vision technologies to analyze human actions in video sequences.
What is the accuracy rate of the action recognition system described in the patent application?
The accuracy rate of the action recognition system is not explicitly mentioned in the abstract. Further details or testing results would be needed to determine the system's accuracy rate.
How does the system handle variations in lighting conditions that may affect action recognition?
The abstract does not provide information on how the system handles variations in lighting conditions. Additional details on the system's robustness to lighting changes would be necessary to understand its performance in different environments.
Original Abstract Submitted
a provided technique accurately recognizes an action of a person even in a time zone in which a region related to an action of the person is not sufficiently shown in an image sequence. an action recognition apparatus includes: a first calculation section for calculating, based on a first partial sequence in an image sequence constituting a time series, first action feature information indicating an action feature of a person included as a subject in an image in the first partial sequence; a second calculation section for calculating, based on past feature information calculated based on a second partial sequence including at least one past image prior to the first partial sequence in the image sequence, second action feature information obtained by correcting the first action feature information; and an action recognition section for recognizing an action of the person based on the second action feature information.
