Automated Video Program Compilation Using Sensor Triggers
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Solution Overview
Problem
There is a challenge in efficiently capturing and compiling edited video programs for multiple individuals participating in athletic activities in large venues, as existing methods are time-consuming and inefficient, especially when dealing with multiple video feeds and large numbers of participants.
Innovation Solution
A system that stores and edits video data from multiple cameras and sensors, using time-stamped sensor data to identify and retrieve relevant video clips for individual users, allowing for the creation of personalized video programs, with options for high and low resolution storage and dynamic generation of low resolution versions for streaming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video data from multiple cameras is continuously stored and manually edited for each participant, then comprehensive video coverage is achieved, but the time required to compile edited video programs increases significantly
Solution Approach 1:
The system performs preliminary actions by continuously recording video data from multiple cameras throughout the event and pre-processing it into organized data files with timestamps. Sensor data is also continuously collected and stored in advance. This preliminary data preparation enables rapid automated compilation later without requiring manual review of entire video feeds.
Solution Approach 2:
The system enables self-service by automatically compiling video programs for each participant based on their sensor data triggers. The automated system retrieves relevant video clips from multiple cameras according to each participant's detected location and time stamps, eliminating the need for manual editing while ensuring complete coverage.
2Manufacturing precision
If high resolution video data is stored for all participants, then video quality is maintained, but storage requirements and system complexity increase
Solution Approach 1:
The system segments video data into separate data files organized by camera identifier and time interval. Each file contains video data from a specific camera for a specific time period, allowing the system to store high resolution data while managing complexity through structured organization and selective retrieval based on participant location.
Solution Approach 2:
The system applies partial action by storing high resolution video data continuously but only retrieving and processing the partial portions relevant to each participant based on sensor triggers. This allows maintaining high quality where needed while reducing overall processing complexity by focusing only on relevant time windows and locations.
3Productivity
If sensor data is used to trigger video recording, then efficient data capture is achieved, but the system must coordinate multiple sensors and cameras with precise timing
Solution Approach 1:
The system uses sensor data as an intermediary that bridges the coordination between multiple cameras. When a sensor detects a participant, it triggers a standardized data retrieval process that queries multiple cameras based on pre-established spatial relationships and time stamps, simplifying the synchronization complexity through a centralized trigger mechanism.
Solution Approach 2:
The system replaces complex mechanical synchronization mechanisms with a software-based time stamp and identifier system. Instead of requiring precise hardware synchronization across multiple cameras, the system uses digital time stamps and camera identifiers to accurately retrieve and assemble video clips, reducing physical complexity while maintaining precision.
Data Source
AI summary
Video and sensor data from multiple locations in a venue, in which multiple individuals are engaged in athletic performances, is stored and edited to create individualized video programs of athletic performances of individuals. Each camera provides a video feed that is continuously stored. Each video feed is stored in a sequence of data files in computer storage, which data files are created in regular time intervals. Each file is accessible using an identifier of the camera and a time interval. Similarly, data from sensors is continuously received and stored in a database. The database stores, for each sensor, an identifier of each individual detected in the proximity of the sensor and the time at which the individual was detected. Each sensor is associated with one or more cameras.