Video Frame Selector for Item Identification in Dynamic Scenes
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Solution Overview
Problem
Current mobile devices struggle to effectively identify and provide information about items in dynamic video frames, requiring users to hold devices steady or record videos instead of taking pictures, which can result in blurry images.
Innovation Solution
A method and system that uses a video processor application with a video frame selector module, item identification module, and location-based incentive module to identify items in video frames, tag them, and offer related offers or incentives based on geographic location, utilizing image recognition algorithms and GPS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users take pictures with mobile devices, then they can capture items, but the user has to hold the mobile device steady and the object needs to remain static to avoid blurry pictures
Solution Approach 1:
The system transitions from static image capture to dynamic video frame processing. Instead of requiring a steady hand and static object for a clear picture, the system processes multiple video frames to identify items, allowing natural hand movement and object motion while maintaining identification accuracy through frame selection and image recognition algorithms.
2Adaptability or versatility
If users record video instead of taking pictures, then they can capture dynamic scenes, but the resulting video frames may be blurry
Solution Approach 1:
The system segments the video into individual frames and selectively processes only certain frames for item identification. By analyzing motion between frames and selecting frames with minimal motion blur, the system captures dynamic scenes effectively while maintaining image clarity for accurate item recognition.
Solution Approach 2:
The system performs preliminary analysis of video frames to identify those with minimal motion blur before proceeding with item identification. By pre-selecting optimal frames based on motion criteria, the system ensures that subsequent image recognition operates on high-quality images, resolving the clarity issue in dynamic video capture.
3Loss of information
If the system processes every video frame, then it can identify all items, but it increases processing time and computational resources
Solution Approach 1:
The system processes only a subset of video frames rather than every frame. By selecting frames based on motion criteria and item presence indicators, the system achieves sufficient item identification completeness while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The system divides the video processing task into frame selection and item identification stages. By first filtering frames based on motion and relevance criteria, then applying image recognition only to selected frames, the system maintains comprehensive item identification while minimizing overall processing time through selective computation.
Data Source
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AI summary
A method and a system are provided. The system comprises a processor-implemented video frame selector module configured to determine a video frame to process that is received from a mobile device, the processor-implemented video frame selector module comprising a video frame analyzer module configured to determine a difference between a first video frame and a second video frame, and a video frame tag module configured to tag the first or second video frame as the determined video frame for item identification when the difference exceeds a predetermined amount. The system further comprises a processor-implemented item identification module configured to identify an item in the determined video frame and to tag the determined video frame with an identification of the item. The system also comprises a processor-implemented market module configured to generate offers of the identified item from at least one merchant to the mobile device.