Adaptive Object Matching Frequency for Video Tracking
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Solution Overview
Problem
Existing systems for tracking individuals in images face challenges such as object switching due to intersection or occlusion, reduced precision due to processing load, and uncertainty in identifying overlapping subjects.
Innovation Solution
A system that determines whether an object in a moving image is a predetermined object by using a determination unit and a setting unit to adjust the frequency of object matching based on division or integration events, optimizing processing load and precision through adaptive frequency setting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If matching is performed for every processed image to maintain high precision, then matching precision is improved, but processing frame rate deteriorates
Solution Approach 1:
The system performs matching periodically at specific intervals rather than continuously for every frame. The matching frequency is dynamically adjusted based on object state - higher frequency when objects are stationary or moving slowly, lower frequency when objects are moving quickly, thereby balancing precision requirements with processing frame rate maintenance
Solution Approach 2:
The matching frequency is made dynamic and adaptable based on real-time conditions. The system adjusts the matching interval according to object motion characteristics, object type, and detection confidence levels, allowing the system to optimize between precision and processing speed for different scenarios
2Productivity
If matching frequency is reduced to maintain processing frame rate, then processing burden is reduced, but matching precision deteriorates
Solution Approach 1:
The system changes the matching frequency parameter dynamically based on multiple factors including object motion speed, object type, detection confidence, and environmental conditions. This allows the system to maintain high precision when needed while reducing processing burden during stable conditions
Solution Approach 2:
The system uses feedback from previous matching results, object tracking consistency, and detection confidence levels to adjust future matching frequencies. When tracking is stable and confidence is high, matching frequency is reduced; when uncertainty increases or objects change state, frequency is increased to maintain precision
3Productivity
If matching is stopped after successful match to reduce processing burden, then processing frame rate is maintained, but tracking reliability deteriorates due to object switching
Solution Approach 1:
The system continuously monitors tracking stability and object state changes as feedback signals. When objects undergo intersection, occlusion, or separation events, the system detects these changes and adjusts matching frequency accordingly to prevent tracking errors and maintain reliability
Solution Approach 2:
The system performs preliminary checks and adjustments before tracking errors can occur. By detecting potential intersection or occlusion events in advance and increasing matching frequency proactively, the system prevents incorrect tracking assignments before they happen
Data Source
AI summary
A setting apparatus, that sets a frequency for determining whether an object in a moving image is a predetermined object, determines whether an object in the moving image is the predetermined object, and when it determines that the object is the predetermined object, it sets the frequency for determining to be lower than before determining that the object is the predetermined object.