Cloud Video Analysis with Dynamic Neural Network Configuration
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Solution Overview
Problem
Conventional techniques fail to effectively analyze video content from devices like cameras and media files to determine objects and their attributes in real-time, requiring expensive hardware and being limited to static images or narrow tasks, unable to process video streams on mobile devices.
Innovation Solution
A cloud-based video processing apparatus that registers configuration information, filters video frames to focus on areas of interest, configures neural networks in parallel or sequential configurations, processes the frames, and provides insight information, allowing for real-time analysis of live or pre-recorded video streams without requiring hardware installation on the video provider side.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional neural networks are used to process video content, then object detection and classification can be achieved, but the system can only process individual images rather than video streams in real-time
Solution Approach 1:
The system segments video streams into individual frames for parallel processing. Each frame is processed independently by neural networks, allowing real-time video analysis by breaking down the continuous video stream into discrete, manageable units that can be handled concurrently.
Solution Approach 2:
The system performs preliminary actions by pre-configuring multiple neural networks in parallel architectures before video processing begins. This pre-arrangement of processing pipelines enables immediate real-time analysis of video streams without setup delays during actual processing.
2Measurement precision
If per-image analysis of video content is provided via neural networks, then analytical features can be obtained, but expensive pre-packaged hardware products with integrated GPUs are required
Solution Approach 1:
The system creates a universal video analysis platform that can perform multiple analytical functions (object detection, classification, tracking) using software-based neural networks. This multi-functional approach eliminates the need for specialized hardware for each specific analysis task, allowing a single system to handle diverse video analysis requirements.
Solution Approach 2:
The system replaces mechanical hardware solutions (integrated GPUs in expensive cameras) with software-based neural network processing. By substituting the hardware-intensive approach with software-based processing, the system achieves comparable or superior analysis accuracy without requiring costly pre-packaged hardware products.
3Productivity
If powerful hardware is used for video processing, then real-time analysis can be achieved, but the solution cannot work on mobile devices with limited resources
Solution Approach 1:
The system implements dynamic resource allocation and adaptive processing that can adjust computational requirements based on the executing platform. This allows the same video analysis system to run on both powerful servers and resource-constrained mobile devices by dynamically optimizing processing parameters, model complexity, and parallelization strategies according to available hardware resources.
Solution Approach 2:
The system changes processing parameters such as neural network model size, batch processing configuration, and parallel computation levels to adapt to different hardware environments. By modifying these parameters, the system maintains real-time processing capability on mobile devices while achieving higher throughput on powerful hardware, thus ensuring broad adaptability across different platforms.
4Reliability
If conventional video processing systems are used, then hardware installation is required on the video provider side, but this increases deployment complexity and cost
Solution Approach 1:
The system introduces a cloud-based intermediary processing layer that receives video streams from various sources without requiring hardware installation on the provider side. This intermediary service handles all neural network processing, allowing video providers to simply upload or stream video content while the complex analysis is performed remotely, thereby simplifying deployment while maintaining processing reliability.
Solution Approach 2:
The system creates virtual copies of the video processing pipeline in the cloud, allowing multiple video streams to be processed simultaneously without duplicating hardware at each source location. This virtualization approach enables reliable processing of numerous video streams while keeping the deployment simple, as no physical hardware needs to be installed or maintained at each video provider site.
Data Source
AI summary
A video processing apparatus includes a registration component that registers configuration information associated with the video information; a filter component that filters the video frame such that all but the area of interest is excluded in a filtered video frame; a configuration component that configures a plurality of neural networks in at least one of a parallel configuration, sequential configuration, mixed parallel and sequential configuration that provides a configured plurality of neural networks; a processing component that processes the filtered video frame using the configured plurality of neural networks that provides insight information; a display that provides insight information to a user; and a storage component that stores the configuration information and insight information in persistent cloud-based storage. A corresponding method of processing video information and computer-readable medium are also disclosed.