Intelligent Video Analysis System Event Prioritization
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Solution Overview
Problem
Current video analysis systems inefficiently process all video data from cameras without prioritization, leading to resource wastage and increased analysis time when a specific event occurs.
Innovation Solution
An intelligent video analysis system that ranks cameras based on event metadata and performs load balancing by assigning more resources to cameras with higher ranks, generating logical camera groups to focus processing on cameras most relevant to the event, thereby optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If videos from all cameras are analyzed without priority, then complete video analysis is performed, but resource consumption increases and analysis time is extended
Solution Approach 1:
The system segments the video analysis task by dividing cameras into different priority groups based on their relevance to detected events. High-priority cameras related to specific events are analyzed first, while low-priority cameras are analyzed later or with reduced resources, thus segmenting the overall analysis workload to improve efficiency without completely sacrificing coverage.
Solution Approach 2:
The system changes the parameter of analysis priority by assigning different rank values to cameras based on event relevance. This parameter change enables dynamic resource allocation where cameras detecting or near events receive higher analysis priority, allowing the system to adapt resource consumption to actual event conditions rather than treating all cameras equally.
2Reliability
If videos from all cameras are analyzed without priority, then all video data is processed, but analysis time is increased
Solution Approach 1:
The video analysis process is segmented into priority-based batches. High-priority video data from cameras relevant to detected events is analyzed immediately, while low-priority video data is analyzed in subsequent batches. This segmentation reduces the time to obtain critical event-related analysis results while maintaining eventual completeness of analysis.
Solution Approach 2:
The system performs preliminary ranking of cameras based on event metadata before video analysis begins. This preliminary action identifies which cameras are most relevant to current events, allowing the analysis system to pre-prepare and prioritize video streams that are most likely to contain event-related information, thereby reducing overall analysis time.
3Ease of operation
If equal resources are allocated to all cameras, then fair processing is provided, but resource allocation efficiency decreases
Solution Approach 1:
The system applies local quality by allocating different resource levels to different cameras based on their local context - specifically, their relevance to detected events. Cameras detecting or near events receive higher resource allocation (higher rank), while other cameras receive lower allocation. This resolves the contradiction by making resource allocation fair in terms of event relevance rather than uniform fairness.
Solution Approach 2:
The resource allocation system is made dynamic by continuously adjusting camera ranks based on real-time event detection and metadata. As events are detected and their characteristics change, the priority ranking of cameras is dynamically updated, allowing resource allocation efficiency to adapt to changing conditions while maintaining operational fairness based on event significance.
Data Source
AI summary
An intelligent video analysis method and system logically selects only surveillance cameras associated with an event and assigns different ranks to the selected surveillance cameras according to the importance thereof. Thereafter, more video analysis resources are assigned to a surveillance camera of high importance, thereby rapidly and efficiently performing video analysis.