18157053. SYSTEMS AND METHODS FOR AUTOMATED OBJECT RECOGNITION simplified abstract (Capital One Services, LLC)
SYSTEMS AND METHODS FOR AUTOMATED OBJECT RECOGNITION
Organization Name
Inventor(s)
Hannes Mikael Jouhikainen of McLean VA (US)
Drew Jacobs of Alameda CA (US)
SYSTEMS AND METHODS FOR AUTOMATED OBJECT RECOGNITION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18157053 titled 'SYSTEMS AND METHODS FOR AUTOMATED OBJECT RECOGNITION
Simplified Explanation
The patent application describes a method for recognizing objects in a video stream using machine learning algorithms.
- The method receives a video stream from a source and selects specific frames based on a frame selection rate.
- The selected frames are then divided into smaller image blocks.
- The machine learning algorithm analyzes these image blocks to identify blocks that contain an image of an object.
- A likelihood metric is calculated by the processor to determine the probability that a specific image block corresponds to the object.
- Information identifying the object is displayed on a display based on the determined likelihood.
Potential Applications
- Video surveillance systems: This technology can be used to automatically detect and recognize objects of interest in surveillance videos, improving security and monitoring capabilities.
- Autonomous vehicles: By recognizing objects in real-time video streams, this method can enhance the perception and decision-making capabilities of autonomous vehicles, enabling them to navigate and respond to their surroundings more effectively.
- Augmented reality: The ability to recognize objects in video streams can be utilized in augmented reality applications, allowing virtual objects to interact with real-world objects in a more realistic and accurate manner.
Problems Solved
- Object recognition in video streams can be challenging due to variations in lighting conditions, object orientations, and background clutter. This method addresses these challenges by using machine learning algorithms to analyze image blocks and determine the likelihood of an object being present.
- Traditional methods of object recognition in videos often require extensive manual annotation and training. This method utilizes machine learning algorithms to automate the recognition process, reducing the need for manual intervention and improving efficiency.
Benefits
- Automation: The method automates the process of object recognition in video streams, reducing the need for manual intervention and saving time and effort.
- Real-time recognition: By analyzing video frames in real-time, this method enables the immediate detection and recognition of objects, allowing for quick response and decision-making.
- Accuracy: The use of machine learning algorithms improves the accuracy of object recognition, reducing false positives and improving the reliability of the system.
Original Abstract Submitted
A method for recognizing an object in a video stream may include receiving a video stream comprising a plurality of video frames from a video source. The method may also select at least one video frame from the video frames according to a frame selection rate. The method may also partition the selected video frame into a first plurality of image blocks, and recognize, out of the first plurality of image blocks, a second plurality of image blocks which comprise an image of an object, the recognition being based on an image recognition parameter determined by a machine-learning algorithm. The method may also determine that at least one of the second plurality of image blocks corresponds to the object based on a likelihood metric, the likelihood metric being determined by the processor based on at least the frame selection rate, and display, on a display, information identifying the object.
