Dynamic Surveillance Video Query via Object Classification
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Solution Overview
Problem
Current video surveillance systems are inefficient in aggregating and querying video content due to their limitations to predefined geographic footprints and time/location-based queries, leading to the need for manual review of extensive irrelevant footage during investigations.
Innovation Solution
A dynamic surveillance system that captures and analyzes video streams from a network of unrelated surveillance devices, using neural networks to classify objects and store data with time, location, and object classification information, enabling object-based queries and efficient retrieval of relevant footage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video surveillance systems use predefined geographic footprints and time/location-based queries, then the system structure is simple and easy to implement, but the efficiency of querying relevant video content deteriorates due to manual review of extensive irrelevant footage
Solution Approach 1:
The system performs preliminary object detection and classification on video content, extracting key features and metadata before queries are executed. This preliminary processing enables rapid retrieval of relevant footage without manual review, directly resolving the contradiction between query efficiency and time consumption.
Solution Approach 2:
The patent introduces an intermediary layer between video capture and query operations - a content analysis system that processes video streams to extract object information, locations, and timestamps. This intermediary enables intelligent querying without requiring manual review of irrelevant footage.
2Adaptability or versatility
If the surveillance system covers a dynamic geographic area with multiple unrelated devices, then the coverage area and versatility improve, but the system complexity increases due to aggregation of video streams from diverse sources
Solution Approach 1:
The system implements a universal architecture that can ingest and process video streams from multiple unrelated surveillance devices with different formats and protocols. This multi-functional design enables dynamic geographic coverage while managing complexity through standardized processing pipelines.
Solution Approach 2:
The patent segments the surveillance system into independent modular components - video ingestion modules, object detection modules, database modules, and query modules. This segmentation allows the system to handle diverse video sources without increasing overall complexity, as each module operates independently with well-defined interfaces.
3Measurement precision
If object-based query is implemented with neural network analysis, then the accuracy of identifying relevant information improves, but the computational resources and processing time required increase
Solution Approach 1:
The system applies partial action by performing object detection and classification only on video segments that are potentially relevant to queries, rather than analyzing every frame of every video stream. This selective processing maintains high identification accuracy while reducing computational resource consumption.
Solution Approach 2:
The patent implements preliminary action by pre-processing video content with neural networks to extract and store object metadata, features, and classifications before queries are executed. This upfront computational investment enables rapid, accurate querying without requiring intensive real-time processing during actual search operations.
Data Source
AI summary
A solution for a video surveillance system and method that leverages a dynamic geographic footprint and supports an object-based query of archived video content is described. An exemplary embodiment of the solution receives video footage from any number of unrelated sources. The video footage is parsed for content and stored in a database in connection with data that identifies the content (object class, aspects of the object, confidence scores, time and location data, etc.). Advantageously, the video footage may be queried based on content of the video footage and not just time and location data. In this way, embodiments of the solution provide for efficient query and review of relevant video footage.