Video Camera Search Optimization Using Semantic Metadata
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Solution Overview
Problem
Current appearance searching technologies in automated security systems face challenges in efficiently identifying and prioritizing video cameras that capture relevant metadata for specific objects of interest across a large video camera universe, leading to suboptimal incident response and forensic investigation efficiency.
Innovation Solution
A computer-implemented method and security system that stores semantic metadata related to video cameras, detects and recognizes object accessories or non-permanent object facets, compares this metadata to identify a subset of cameras meeting a similarity score condition, and creates or modifies analytics criteria for prioritizing these cameras, thereby optimizing the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If appearance searching is performed across the entire video camera universe, then comprehensive search coverage is achieved, but search time and computational resources increase significantly
Solution Approach 1:
The patent segments the video camera universe into multiple zones based on semantic metadata characteristics. By dividing the search space into manageable segments and applying targeted analytics criteria to each zone, the system achieves comprehensive coverage while reducing overall search time and computational resources required.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing semantic metadata for each video camera zone before the actual appearance search occurs. This includes pre-calculating similarity scores and organizing cameras into prioritized subsets, so that when a search is initiated, the system can quickly retrieve and compare against pre-organized data rather than processing everything in real-time.
2Measurement precision
If semantic metadata is stored and compared for all video cameras, then accurate camera identification is achieved, but data processing complexity increases
Solution Approach 1:
The patent applies local quality by creating analytics criteria that are specific to each video camera zone's semantic metadata characteristics. Instead of using a uniform search approach across all cameras, the system tailors the search parameters and prioritization rules to match the local properties of each zone, improving identification accuracy while managing complexity through localized processing.
Solution Approach 2:
The system changes parameters by dynamically adjusting analytics criteria based on the semantic metadata being searched. The similarity score thresholds, prioritization rules, and search parameters are modified according to the specific characteristics of the object being searched for and the zones being examined, allowing accurate identification without requiring equally complex processing for all scenarios.
3Productivity
If analytics criteria are created to prioritize video cameras, then search efficiency is improved, but system configuration complexity increases
Solution Approach 1:
The system performs preliminary action by pre-defining analytics criteria and prioritization rules for different zone types and search scenarios. These configurations are established in advance and stored in the system, allowing operators to quickly initiate searches without manually configuring complex parameters each time. The pre-configured criteria enable efficient search execution while minimizing the operational complexity users must manage.
Data Source
AI summary
A method and system for profiling a reference image and an object-of-interest therewithin is disclosed. The method includes storing semantic metadata that provides semantic information about environments for respective areas covered by respective Fields Of View (FOVs) of video cameras. The method also includes comparing the semantic metadata to additional metadata corresponding to at least one of an object accessory and a non-permanent object facet to identify a subset of the video cameras, less than an entire video camera universe, that meet a similarity score condition.