Intelligent Video Analysis for Mobile Camera Tracking
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Solution Overview
Problem
Existing automated video analysis systems are limited to stationary cameras and primarily perform object detection, lacking the ability to handle moving cameras and provide comprehensive functions like tracking, activity recognition, and semantic relationship analysis in video feeds.
Innovation Solution
The system employs machine learning algorithms for real-time video analysis, integrating advanced computer vision capabilities for object detection, tracking, geo-registration, activity recognition, and semantic parsing, enabling support for both stationary and mobile camera platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated video analysis systems are used, then manpower requirements are reduced, but the systems are limited to stationary cameras and basic object detection only
Solution Approach 1:
The video analysis system is designed to perform multiple functions including object detection, tracking, activity recognition, and semantic relationship analysis. The system can handle both stationary and moving cameras, making it universally applicable across different camera types and scenarios, thereby resolving the limitation of existing systems that work only for stationary cameras and basic detection.
2Measurement precision
If manual video analysis is performed, then comprehensive analysis can be conducted, but large amounts of video data become impractical for human operators to analyze
Solution Approach 1:
The system replaces manual human analysis with automated computer vision and machine learning algorithms. This substitution enables the processing of large volumes of video data that would be impractical for human operators, while maintaining comprehensive analysis capabilities through advanced algorithms for object detection, tracking, and semantic understanding.
3Productivity
If real-time video processing is implemented, then monitoring efficiency is improved, but computational resources and processing complexity increase
Solution Approach 1:
The video analysis process is segmented into distinct functional modules including object detection, object tracking, activity recognition, and semantic relationship analysis. This modular segmentation allows real-time processing by dividing the complex task into manageable components, each handled by specialized algorithms, thereby improving real-time monitoring efficiency while managing computational complexity.
Data Source
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AI summary
An apparatus is provided. The apparatus receives a video feed and processes the video feed in real-time as the video feed is received. The apparatus performs object detection and recognition on the video feed to detect and classify objects therein, performs activity recognition to detect and classify activities of at least some of the objects, and outputs classified objects and classified activities in the video feed. The apparatus generates natural language text that describes the video feed, produces a semantic network, and stores the video feed, classified objects and classified activities, natural language text, and semantic network in a knowledge base. The apparatus generates a graphical user interface (GUI) configured to enable queries of the knowledge base, and presentation of selections of the video feed, classified objects and classified activities, natural language text, and semantic network.