Geo-tagging Detected Objects in Video Surveillance
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Solution Overview
Problem
Current video surveillance systems face difficulties in searching through video data for specific incidents due to the lack of precise geo-tagging of detected objects, as GPS coordinates only provide location information for the camera and not the objects within the video.
Innovation Solution
A camera system comprising a sensor, interface, and processor that generates metadata for detected objects, including their location relative to the sensor, allowing for bulk searchable geo-tagging and real-time analysis of objects like vehicles, license plates, and faces, with the ability to estimate distance and orientation, and append metadata to video files for easy database searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS coordinates are used to geo-tag video footage, then the location of the capturing camera is recorded, but the exact location and timestamp of detected objects (faces, license plates) cannot be determined
Solution Approach 1:
The system segments the video content by detecting and identifying specific objects (faces, license plates, vehicles) within the video stream. Each detected object is processed independently to extract its specific location, orientation, and timestamp information, separating object-level data from camera-level GPS coordinates.
Solution Approach 2:
The system adds temporal dimension (timestamp) and object-relative spatial dimensions (orientation, distance) to the traditional GPS coordinate system. This creates a multi-dimensional location system that includes both the camera's GPS position and the detected object's position relative to the camera, enabling precise object location determination.
2Quantity of substance
If video data is stored and archived in large quantities, then more video content is available, but the ability to search for specific videos becomes more difficult
Solution Approach 1:
The system performs preliminary processing of video data by detecting objects, extracting their features (faces, license plates), determining their locations and orientations, and generating metadata tags before the video is stored. This pre-processing creates an indexed structure that enables efficient searching without requiring manual review of archived videos.
Solution Approach 2:
The system introduces metadata as an intermediary layer between the video data and the search function. This metadata includes object detection results, location information, timestamps, and orientation data, which serve as searchable indices that bridge the gap between large video archives and efficient query capabilities.
3Measurement precision
If object detection and metadata generation are performed for every video frame, then precise object tracking is achieved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing object detection and metadata generation only on specific frames or events of interest rather than processing every single frame uniformly. This selective processing maintains detection accuracy for critical moments while reducing overall computational burden and processing time.
Data Source
AI summary
An apparatus comprising a sensor, an interface and a processor. The sensor may be configured to generate a video signal based on a targeted view of an environment. The interface may be configured to receive status information of the apparatus at a time of generation of the video signal. The processor may be configured to (i) detect one or more objects in the video signal, (ii) determine a location of the one or more objects relative to the sensor and (iii) generate metadata. The location of the one or more objects may be based on (i) the status information of the apparatus and (ii) a field of view of the sensor. The metadata may correspond to the location of the one or more objects.