Automated Object Recognition in Video Streams Using Frame Segmentation
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Solution Overview
Problem
Current content delivery systems, such as Netflix and Amazon Prime, do not provide automated recognition or identification of products shown in video or image content, limiting user information and sales opportunities for brands and merchants.
Innovation Solution
The development of systems and methods for automated object recognition in video and image content, using a processor to analyze video streams, select frames, partition them into image blocks, and apply machine-learning algorithms to recognize objects and display identifying information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If automated object recognition is implemented in video streams, then user information and sales opportunities are improved, but computing power requirements increase significantly
Solution Approach 1:
The video stream is divided into individual frames, and each frame is further segmented into multiple image blocks or regions. This segmentation allows the system to process only relevant portions of each frame rather than the entire video stream, reducing computational load while maintaining recognition accuracy.
Solution Approach 2:
The system performs recognition on a subset of video frames rather than every frame, using frame selection based on motion detection or key frame identification. This partial action approach reduces the total number of frames processed while still capturing all relevant objects, thereby reducing computing power requirements.
2Measurement precision
If every video frame is analyzed for object recognition, then recognition accuracy is improved, but processing time increases
Solution Approach 1:
The system analyzes video frames at periodic intervals rather than continuously processing every frame. By selecting key frames based on temporal spacing or motion changes, the system maintains recognition accuracy for moving objects while significantly reducing processing time and computational overhead.
Solution Approach 2:
The system performs preliminary analysis on video frames to identify those containing objects of interest before applying full recognition algorithms. This preliminary screening through motion detection or simple feature analysis filters out frames that don't require detailed processing, reducing overall processing time while maintaining accuracy for relevant frames.
3Reliability
If the entire video frame is processed, then complete object detection is improved, but computational complexity increases
Solution Approach 1:
The video frame is divided into multiple image blocks or regions of interest. The system processes each segment independently using simplified recognition models, then combines results to achieve complete object detection. This segmentation reduces the complexity of processing each individual region while maintaining overall detection completeness.
Solution Approach 2:
The system extracts and processes only the relevant regions or blocks from each video frame that contain potential objects, rather than analyzing the entire frame uniformly. This extraction approach reduces computational complexity by focusing resources on areas of interest while still ensuring complete object detection across the full frame.
Data Source
AI summary
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.