Adaptive Video Processing for Object Confidence
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Solution Overview
Problem
Current video processing systems for object and event recognition in surveillance applications often require multiple operators and can become overwhelmed during high video activity, leading to missed important events due to low confidence in object and event identification.
Innovation Solution
An adaptive video processing system that repeatedly processes video frames to increase object and event confidence values using algorithms like super-resolution and blur reduction, and identifies events through a hierarchical structure based on object relationships using Bayesian networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated video processing is implemented to reduce operator load, then the number of operators required decreases, but the system may become overwhelmed during high video activity and miss important events
Solution Approach 1:
The system dynamically adjusts processing parameters and confidence thresholds based on video activity levels and scene complexity. When activity increases, the system adapts by selectively applying processing algorithms to regions of interest rather than uniformly processing all frames, maintaining reliability while managing computational load during high-volume surveillance operations
Solution Approach 2:
The system changes processing parameters such as confidence thresholds, processing intensity, and algorithm selection based on contextual factors including video quality, scene complexity, and detected object types. This allows the automated system to maintain high detection reliability across varying operational conditions without requiring proportional increases in operator staffing
2Productivity
If confidence thresholds for object identification are lowered to detect more events, then more events are captured, but false positive identification increases
Solution Approach 1:
The system employs feedback mechanisms where detection results are continuously evaluated and used to adjust processing parameters. When confidence values fall below optimal thresholds, the system applies additional processing algorithms or requests re-examination of specific regions, thereby maintaining measurement precision while preserving the ability to detect low-confidence but potentially important events
Solution Approach 2:
The system performs preliminary processing and confidence assessment on video frames before final event determination. Objects or regions with marginal confidence values undergo additional processing steps such as enhanced feature extraction or comparison with reference databases, ensuring that productivity gains do not compromise identification accuracy
3Measurement precision
If additional processing algorithms are applied to low-confidence detections, then identification accuracy improves, but processing time and computational load increase
Solution Approach 1:
The system applies additional processing algorithms selectively only to regions or objects with confidence values below a specified threshold, rather than uniformly processing all video content. This localized approach improves identification confidence for uncertain detections while minimizing the time and computational resources consumed by the system overall
Solution Approach 2:
The system applies processing algorithms with varying intensity based on confidence levels. For low-confidence detections, more extensive processing is applied; for high-confidence detections, minimal or no additional processing is performed. This partial action strategy optimizes the balance between measurement precision and time loss by avoiding unnecessary processing of already well-identified objects
Data Source
AI summary
A method for adaptive video processing includes identifying an object in a frame of video data, determining an object confidence value associated with the identified object, and determining if the object confidence value meets a predefined threshold object confidence value. If not, the method can further include repeatedly processing the frame of video data with additional processing algorithms, and again identifying the object, determining an updated object confidence value, and determining if the updated object confidence value meets the predefined threshold object confidence value, until the updated object confidence value meets the predefined threshold object confidence value. Thereafter, an event can be identified based upon the identified object and an object primitive defining a relationship between the identified object and one or more other objects in the frame of video data, where the events can be identified in accordance with a hierarchical event structure.