Cloud-Based 3D Analytics from 2D Surveillance Video
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Solution Overview
Problem
Current video surveillance systems rely on 2D images and videos, limiting their ability to provide advanced analytics and requiring costly upgrades to 3D cameras for improved security and surveillance capabilities.
Innovation Solution
A cloud-based surveillance system that generates 3D surveillance data from 2D input data using calibrated cameras and advanced image processing, enabling 3D analytics and playback for enhanced security and surveillance applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D cameras are used to generate 3D surveillance data, then measurement precision and analytics accuracy are improved, but device cost and complexity increase significantly
Solution Approach 1:
The patent creates a virtual 3D copy of the surveillance scene by processing 2D images from multiple cameras through stereo vision algorithms and neural networks. Instead of using expensive 3D cameras, the system generates synthetic 3D point cloud data that replicates the spatial information, thereby achieving 3D analytics accuracy without the high cost and complexity of true 3D imaging hardware
Solution Approach 2:
The system transforms 2D image data from multiple camera angles into 3D spatial representations by adding the depth dimension through computational methods. This dimensionality conversion allows the system to achieve 3D surveillance analytics using standard 2D cameras, avoiding the need for specialized 3D imaging equipment while maintaining measurement precision
2Device complexity
If multiple 2D cameras are calibrated and processed through cloud-based analytics, then 3D surveillance capability is achieved without expensive hardware, but system complexity and processing requirements increase
Solution Approach 1:
The patent extracts the complex processing functions from the local camera systems and relocates them to a cloud-based platform. The 2D cameras perform only simple image capture, while the computationally intensive tasks of stereo calibration, point cloud generation, and 3D analytics are performed remotely in the cloud, thereby reducing local system complexity while maintaining high automation through centralized cloud processing
Solution Approach 2:
The system introduces a cloud-based processing platform as an intermediary between the 2D cameras and the final 3D surveillance output. This intermediary handles the complex coordination of multiple cameras, performs stereo vision calculations, and generates 3D point clouds, thereby simplifying the overall system architecture while enabling automated 3D analytics without requiring complex local processing at each camera
Data Source
AI summary
Systems and methods for cloud-computing network with distributed input devices and a cloud-based analytics platform for automatically analyzing received 2-Dimensional (2D) video and/or image inputs for generating 3-Dimensional (3D) surveillance data and providing 3D analytics for a target surveillance area.