Camera Footage Synchronization via Timestamp Correction
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Solution Overview
Problem
Surveillance cameras often record inaccurate metadata for date and time, leading to difficulties in synchronizing footage during investigations, which is time-consuming and prone to human error, especially when multiple cameras need to be synchronized.
Innovation Solution
A method and system that utilize a processing device to identify and correct camera metadata by associating each camera with a correction to date and time, facilitating synchronization across multiple cameras, allowing for quick location and viewing of relevant footage by applying adjustments to metadata stored in a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual date and time setting is used in cameras, then camera operation is simple and inexpensive, but metadata accuracy deteriorates due to human error and power outage failures
Solution Approach 1:
The system enables cameras to automatically correct their own metadata errors by computing correction factors based on known good timestamps (from incident reports or other synchronized sources) and applying these corrections automatically to their footage timestamps, eliminating the need for manual investigator calculations
Solution Approach 2:
A centralized processing system acts as an intermediary between multiple cameras and the investigation database, automatically computing and storing correction factors for each camera based on their metadata errors, thereby mediating the synchronization across all cameras without manual intervention at each camera
2Adaptability or versatility
If investigators manually calculate and correct timestamps for multiple cameras, then flexibility in handling different camera errors is maintained, but time consumption and human error increase significantly
Solution Approach 1:
The system performs preliminary correction by pre-computing and storing correction factors for each camera in the database before the actual footage review process, so that when investigators need to synchronize cameras, the corrections are already prepared and can be applied instantly without manual calculation
Solution Approach 2:
The processing system automatically identifies cameras with metadata errors, computes their specific correction factors, and applies them systematically, allowing the system to serve itself in correcting multiple cameras with different error patterns without requiring investigator intervention for each camera
3Productivity
If camera metadata is not corrected, then footage can be viewed immediately without processing, but the veracity and reliability of video evidence deteriorates
Solution Approach 1:
The system performs preliminary correction of camera metadata by computing and storing correction factors in the database before evidence review, so that when footage is accessed, the corrections are already in place and can be applied automatically, ensuring both rapid access and reliable evidence
Solution Approach 2:
The system establishes feedback by comparing camera metadata timestamps against known accurate timestamps (from incident reports or synchronized sources), computing the discrepancy, and using this feedback to automatically generate and apply correction factors, thereby continuously ensuring evidence reliability
Data Source
AI summary
A method and system for synchronizing camera footage from a plurality of cameras includes providing a database of cameras, accessible via a user device, which stores a correction associated with each camera to a date/time in metadata associated with footage recorded by the camera, to facilitate synchronization of footage recorded by each camera to an actual date/time. The stored correction is applied to camera(s) to determine an adjusted date/time in the metadata corresponding to footage recorded at a particular date and time; and the footage for the camera(s) is synchronized to the particular date and time, based on the adjusted date/time determined from the correction. Cameras for synchronization are identified based on location stored in the database. A list of identified cameras may be exported with a case or UserID, and locations, adjusted metadata for synchronization, and bases for the correction calculation. The list is optionally generated via mapping functions.