18183881. METHOD AND DEVICE WITH OBJECT RECOGNITION FOR INFORMATION COLLECTED FROM MOVING OBJECT simplified abstract (Samsung Electronics Co., Ltd.)
Contents
- 1 METHOD AND DEVICE WITH OBJECT RECOGNITION FOR INFORMATION COLLECTED FROM MOVING OBJECT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND DEVICE WITH OBJECT RECOGNITION FOR INFORMATION COLLECTED FROM MOVING OBJECT - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD AND DEVICE WITH OBJECT RECOGNITION FOR INFORMATION COLLECTED FROM MOVING OBJECT
Organization Name
Inventor(s)
Seung Tae Khang of Suwon-si (KR)
Young Rae Cho of Suwon-si (KR)
METHOD AND DEVICE WITH OBJECT RECOGNITION FOR INFORMATION COLLECTED FROM MOVING OBJECT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18183881 titled 'METHOD AND DEVICE WITH OBJECT RECOGNITION FOR INFORMATION COLLECTED FROM MOVING OBJECT
Simplified Explanation
The abstract describes a method and device for object recognition using information collected from a sensor of a moving object. The method involves identifying a target with a confidence level of recognition accuracy less than a threshold, obtaining second sensing information on the target from another moving object, synchronizing the collected information based on time data, and training a neural network for estimating recognition information of the target.
- Object recognition method for information collected from a sensor of a moving object
- Identifying a target with a confidence level of recognition accuracy less than a threshold
- Obtaining second sensing information on the target from an interworking moving object
- Synchronizing the collected information based on time data
- Training a neural network for estimating recognition information of the target
Potential Applications
This technology can be applied in:
- Autonomous vehicles for object recognition and tracking
- Surveillance systems for identifying targets in real-time
- Robotics for detecting and interacting with objects in the environment
Problems Solved
This technology helps in:
- Improving accuracy in object recognition
- Enhancing the capabilities of sensor-based systems
- Enabling efficient data synchronization for better analysis
Benefits
The benefits of this technology include:
- Increased efficiency in object recognition tasks
- Enhanced reliability in identifying targets
- Improved overall performance of sensor-based devices
Potential Commercial Applications
This technology has potential commercial applications in:
- Automotive industry for autonomous driving systems
- Security and surveillance industry for advanced monitoring solutions
- Robotics industry for intelligent automation processes
Possible Prior Art
One possible prior art for this technology could be the use of neural networks for object recognition in sensor-based systems.
Unanswered Questions
How does the method handle complex environments with multiple moving objects?
The abstract does not specify how the method deals with scenarios where there are multiple moving objects in the environment. It would be interesting to know how the system distinguishes between different targets in such situations.
What is the computational complexity of training the neural network for object recognition?
The abstract does not mention the computational resources required for training the neural network. Understanding the computational complexity can provide insights into the feasibility of implementing this technology in real-world applications.
Original Abstract Submitted
A method and device for object recognition for information collected from a sensor of a moving object are disclosed. The method may include identifying a target with a confidence level of recognition accuracy less than a threshold, based on first sensing information collected from a moving object, obtaining second sensing information on the target from an interworking moving object, synchronizing the first sensing information and the second sensing information based on time information included in the first sensing information and time information included in the second sensing information, and training a neural network for estimating recognition information of the target from the first synchronized sensing information by using information on the target included in the second synchronized sensing information as ground truth data.
