Video Stream Object Identification via Timestamped Pre-processing
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Solution Overview
Problem
Existing technologies face challenges in efficiently processing video streams and associated requests in real-time, particularly in identifying and providing information about items depicted within the streams, due to limitations in network compatibility and communication latency, leading to increased network congestion and inaccurate data storage.
Innovation Solution
The implementation of a system that uses machine learning models to pre-process video streams, determine candidate objects, and transmit relevant information to display and voice-enabled devices, allowing for accurate and timely identification of items within the streams, reducing latency and network congestion by coordinating information between devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time processing of video streams and requests is implemented, then user information needs are met timely, but network congestion increases and processing accuracy decreases
Solution Approach 1:
The system performs preliminary processing by pre-generating candidate object lists and storing them with timestamp associations before requests arrive. When a request is received, the system simply retrieves and matches the pre-prepared candidate list with the request timestamp, eliminating the need for real-time complex processing and thereby reducing both response time and network congestion while maintaining accuracy.
Solution Approach 2:
The video stream processing is segmented into discrete timestamped frames, with candidate objects identified and stored for each frame independently. This segmentation allows the system to process only the specific timestamped segment relevant to each request, reducing overall processing complexity and network load while maintaining precise temporal accuracy for each segment.
2Measurement precision
If complex processing is performed to identify items in video streams, then identification accuracy improves, but network bandwidth consumption increases
Solution Approach 1:
The system performs the complex identification processing in advance, generating and storing candidate object lists with their associated timestamps before actual user requests arrive. This preliminary action shifts the computational burden from real-time request processing to offline pre-processing, thereby maintaining high identification accuracy while minimizing real-time network bandwidth consumption.
Solution Approach 2:
The system creates and stores copies of candidate object information indexed by timestamp. Instead of re-processing video data for each request, the system retrieves pre-generated candidate lists that are copied and matched with incoming requests based on timestamp, thereby maintaining identification accuracy while dramatically reducing network bandwidth usage during request handling.
3Quantity of substance
If more memory resources are allocated for data storage, then data availability improves, but system complexity increases
Solution Approach 1:
The stored data is segmented into discrete timestamped entries, each containing candidate object information for a specific video frame. This segmentation allows the system to store and manage data in organized, time-indexed units, improving data retrieval efficiency and availability while keeping the storage architecture simple and manageable through clear temporal organization.
Data Source
AI summary
A voice-enabled device and a display device may be utilized to determine items in video output by the display device. A frame of a video stream associated with the video may be utilized to determine a representation of a candidate object. A stream identifier and a timestamp associated with the candidate object in the video stream may be determined. The stream identifier, the timestamp, and an object identifier associated with the candidate object may be stored in a database. A first request to output the video stream via a display device may be received. A second request associated with the video stream may be received while the video stream is being output by the display device. The second request may be determined to be associated with the representation of the candidate object. The object identifier may be caused to be visually displayed via the display device.