Camera Apparatus Object Recognition Data Separation
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Solution Overview
Problem
Existing video surveillance systems, particularly those used in mobile devices like body-worn cameras, face challenges in providing real-time facial recognition due to limited processing capability and bandwidth constraints, making it difficult to effectively recognize objects over low-quality communication channels.
Innovation Solution
A video surveillance system that utilizes a camera apparatus connected to a central server via a communication channel, where the camera captures and encodes both video and object recognition data, allowing for object recognition even with low-bandwidth communication by using a two-stage process involving object detection and classification, with initial detection occurring locally and further processing at the server for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If video data is transmitted over low-bandwidth communication channels, then communication efficiency is improved, but object recognition accuracy deteriorates
Solution Approach 1:
The system segments video data transmission into two distinct streams: compressed video data for surveillance purposes and extracted object recognition data for identification purposes. This allows the compressed video stream to maintain low bandwidth consumption while the object recognition stream provides sufficient detail for accurate identification, resolving the contradiction between bandwidth efficiency and recognition accuracy
Solution Approach 2:
The system extracts object recognition data from the video stream at the camera apparatus before transmission. By taking out only the essential recognition features and transmitting them separately, the system achieves accurate object recognition without requiring transmission of full-quality video data, thus maintaining low bandwidth usage while improving recognition accuracy
2Productivity
If processing capability is increased for real-time object recognition, then recognition speed is improved, but device complexity increases
Solution Approach 1:
The system segments processing tasks between the camera apparatus and the central server. The camera performs lightweight object detection and data extraction, while the server handles complex recognition and database matching. This distribution allows real-time recognition speed without requiring the mobile device to have high processing capability
Solution Approach 2:
The camera apparatus performs self-service by automatically detecting objects and extracting recognition data from the video stream without requiring complex processing at the central server. This reduces the processing burden on the server and enables faster real-time recognition while keeping the camera apparatus relatively simple
Data Source
AI summary
An object recognition enabled, for example facial recognition enabled, video surveillance system for capturing video of a scene and allowing recognition of objects within that scene. The system comprises at least one camera apparatus connected via a communication channel to a central server with the camera apparatus arranged for capturing visual representation data of a scene. The visual representation data comprises video of the scene and the camera apparatus comprises a camera for capturing said video and a video encoder for sending corresponding video data via the communication channel to the central server. The camera apparatus is further arranged for generating object recognition data based on said visual representation data, and the video encoder is arranged to send said object recognition data along with the video data via the communication channel.