Automated Object Tracking in Live Video Feeds
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Solution Overview
Problem
Current video surveillance systems face inefficiencies in processing and maintaining vast amounts of video data, leading to missed insights and requiring manual human analysis for tasks like object recognition and anomaly detection, which is time-consuming and prone to errors.
Innovation Solution
An automated system using computer vision, natural language processing, and machine learning to track objects and activities in live video feeds, generating natural language text that describes the storyline and allowing for user input to focus on specific objects or activities, with the ability to predict future outcomes and alert users to anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human analysis is used for object recognition and anomaly detection, then detection accuracy can be maintained, but processing time and operational cost increase substantially
Solution Approach 1:
The patent replaces manual human analysis with an automated computer vision system that uses deep learning neural networks to detect objects and activities in real-time video feeds, eliminating the need for human operators while maintaining detection accuracy
Solution Approach 2:
The system automatically processes video feeds, identifies objects and activities, generates alerts, and provides notifications without requiring human intervention, enabling the surveillance system to serve itself continuously
2Productivity
If automated systems are implemented to process video data, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements a unified automated surveillance system that performs multiple functions including object detection, activity recognition, anomaly detection, and real-time alert generation within a single integrated platform, reducing overall system complexity despite the multitude of capabilities
Solution Approach 2:
The system introduces an intermediary processing layer that automatically analyzes video feeds and generates structured alerts, acting as a bridge between raw video data and user notification, which simplifies the interaction complexity for users
3Reliability
If real-time automated tracking is implemented, then situational awareness increases, but computational resource consumption increases
Solution Approach 1:
The patent applies computer vision models selectively to specific regions and objects of interest within the video feed rather than processing the entire video uniformly, optimizing computational resource allocation to maintain high situational awareness for critical elements
Solution Approach 2:
The system pre-identifies and tracks objects and activities of interest before anomalies occur, maintaining continuous situational awareness through proactive monitoring rather than reactive analysis, which optimizes computational efficiency by focusing resources on high-value targets
Data Source
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AI summary
An apparatus is provided for automated object and activity tracking in a live video feed. The apparatus receives and processes a live video feed to identify a plurality of objects and activities therein. The apparatus also generates natural language text that describes a storyline of the live video feed using the plurality of objects and activities so identified. The live video feed is processed using computer vision, natural language processing and machine learning, and a catalog of identifiable objects and activities. The apparatus then outputs the natural language text audibly or visually with a display of the live video feed.