Multi-Camera IP Video Analysis for Automated Event Detection
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Solution Overview
Problem
Existing IP video camera networks in businesses, such as car dealerships and restaurants, are primarily installed for security but lack the capability to automatically analyze and recognize human and object activities relevant to specific business events, leading to inefficiencies and potential customer dissatisfaction.
Innovation Solution
A method utilizing an IP video camera network with passive cameras connected to a GPU server and visual AI engine to detect and process video streams, generating a synthetic stream that identifies and tracks human and object activities, aligning camera views, and providing actionable insights through reports or notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple IP video cameras are installed to acquire comprehensive video stream data from different areas, then the quantity and coverage of video data increase, but the complexity of analyzing and processing the vast amount of data increases significantly
Solution Approach 1:
The patent segments the video stream data from multiple cameras into individual streams, processing each separately through AI analysis before integrating results. This divides the complex task of analyzing all cameras simultaneously into manageable individual camera analyses, reducing overall system complexity while maintaining comprehensive coverage
Solution Approach 2:
The patent introduces an intermediary processing system that receives video streams from multiple cameras, performs AI-based object detection and activity recognition on each stream, and then integrates the results. This intermediary layer handles the complexity of multi-camera coordination, allowing the system to scale without proportionally increasing analysis difficulty
2Loss of information
If human reviewers manually analyze multiple video streams to identify problems, then actionable insights can be obtained, but the time required and human attention limits cause delays in detecting events
Solution Approach 1:
The patent replaces the mechanical human review process with an automated AI-based video analysis system. The system uses computer vision algorithms to detect objects, track movements, and recognize activities across multiple video streams simultaneously, eliminating human attention limits and significantly reducing detection time while maintaining or improving detection completeness
Solution Approach 2:
The system enables self-service automated analysis where the video surveillance system automatically detects and reports events without requiring human intervention for initial screening. The AI engine continuously monitors all camera feeds, identifies relevant events, and generates alerts, allowing the system to serve its own monitoring needs autonomously
3Productivity
If computer vision technology is used to automate video stream analysis, then processing speed increases, but the technology is not yet advanced enough to provide full scope of useful information needed
Solution Approach 1:
The patent applies partial automation where AI handles specific detection tasks (object identification, activity recognition) that can be reliably automated, while maintaining the option for human review when complex judgment is needed. This partial action approach achieves speed improvements for routine detections without sacrificing completeness for complex events
Solution Approach 2:
The system adjusts analysis parameters dynamically based on the specific event type and context. Different detection sensitivity levels, tracking parameters, and analysis depths are applied depending on the situation, allowing the system to optimize between speed and information completeness for different types of events rather than using a fixed analysis approach
4Area of stationary object
If cameras are positioned to maximize field of vision coverage, then surveillance coverage improves, but the camera views may not be optimally aligned for automated activity recognition and synthesis
Solution Approach 1:
The patent performs preliminary alignment and calibration of camera views during system setup, establishing coordinate transformations and spatial relationships between cameras before actual monitoring begins. This preliminary action creates a standardized framework that simplifies subsequent automated analysis, allowing cameras to be positioned for maximum coverage while maintaining analytical compatibility through pre-computed transformation parameters
Data Source
AI summary
Various examples are related to identifying and monitoring an event of interest at a location, such as a business. In one example, a method includes providing an IP camera network including passive IP enabled video cameras installed at a location. The method includes identifying an event of interest for monitoring by acquiring video stream information from one or more of the passive IP enabled video cameras; transmitting the video stream information to a network video recorder (“GPU server”) device; and processing the video stream information via a visual artificial intelligence engine. The method can generate a start time for the identified event; acquire additional video stream information associated with the identified event; transmit the additional video stream information to the GPU server device; process the additional video stream information via the visual artificial intelligence engine; and generate information associated with the identified event with can be configured for use.