Visual Event Detection via Adaptive Image Region Processing
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Solution Overview
Problem
Current image processing systems lack the ability to selectively process images based on identified objects and scene information, leading to inefficient event detection and analysis.
Innovation Solution
The system captures and analyzes images using image sensors, identifies objects and regions, selects appropriate processing schemes, and applies inference models based on scene information to detect events and activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform processing is applied to all image regions, then processing simplicity is maintained, but processing efficiency deteriorates due to unnecessary computation in irrelevant regions
Solution Approach 1:
The image is divided into multiple regions of interest based on detected objects. Different processing schemes are applied to different regions, allowing efficient processing of only relevant areas while maintaining simplicity in each individual region's processing.
Solution Approach 2:
Different processing schemes are selected and applied to different regions of the image based on the specific objects and scene information detected in each region. This allows each region to receive processing optimized for its specific content, improving overall efficiency without requiring complex global processing.
2Measurement precision
If comprehensive scene analysis is performed, then event detection accuracy is improved, but processing time increases
Solution Approach 1:
Scene information and object detection are performed as preliminary steps before applying specific processing schemes. This preliminary analysis enables targeted processing that achieves high event detection accuracy while minimizing processing time by avoiding unnecessary comprehensive analysis in all regions.
Solution Approach 2:
The system uses detected objects and scene information to dynamically select processing schemes, creating a feedback loop where detection results inform subsequent processing decisions. This ensures accurate event detection while optimizing processing time through adaptive scheme selection.
3Adaptability or versatility
If multiple processing schemes are maintained for different scenarios, then processing adaptability is improved, but system complexity increases
Solution Approach 1:
A single image processing system is designed to perform multiple functions by dynamically selecting from different processing schemes based on detected scene information. This multi-functionality approach provides adaptability across different scenarios without requiring separate dedicated systems for each scenario.
Solution Approach 2:
The processing scheme selection is made dynamically based on real-time object detection and scene analysis results. This dynamic adaptation allows the system to respond to different scenarios flexibly while maintaining a unified system architecture, avoiding the complexity of multiple static systems.
Data Source
AI summary
System and method for image processing are provided. A stream of images may be obtained, for example by capturing images using an image sensor. Points in time associated with an activity may be obtained. For each point in time, the stream of images may be analyzed to identify events related to the activity and preceding the point in time. Based on the identified events, an event detection rule configured to analyze images to detect at least one event may be obtained. Image data may be obtained, and the image data may be analyzed using the event detection rule to detect events matching selected criteria in the image data.