Object Tracking via Color Resemblance Threshold and Location Data
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Solution Overview
Problem
Existing image analysis techniques for object and people tracking in video surveillance systems face challenges in accurately identifying and distinguishing between similar objects or people across different images, especially when movement and location data are considered, leading to potential errors in tracking and monitoring.
Innovation Solution
A method that associates color characteristics with identified objects or people in images, using a color resemblance threshold to determine similarity, and updates tracking records with time and location data to generate accurate paths and conveyance of movement information, while considering movement and location information to differentiate between new and existing objects or people.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If color characteristics alone are used to identify objects or people across images, then tracking can be performed, but accuracy deteriorates due to inability to distinguish between similar objects or people
Solution Approach 1:
The patent combines multiple identification features (color characteristics, location data, time information, movement patterns) into a unified tracking system. By merging these different types of data, the system achieves more accurate object identification than any single feature could provide alone, resolving the contradiction between tracking reliability and identification precision.
Solution Approach 2:
The patent adds temporal and spatial dimensions to the tracking process by incorporating time information and location data alongside color characteristics. This multi-dimensional approach allows the system to distinguish between similar objects by considering their position and movement over time, not just their appearance.
2Measurement precision
If multiple features (color, location, time, movement) are combined to improve tracking accuracy, then identification precision improves, but system complexity increases
Solution Approach 1:
The patent segments the tracking process into distinct functional modules: color characteristic extraction, location determination, time stamping, movement analysis, and integrated identification. This modular segmentation reduces system complexity by making each component independent and manageable while maintaining overall high identification precision.
3Productivity
If color resemblance threshold is set low to include more potential matches, then tracking coverage improves, but error rate increases due to false positives
Solution Approach 1:
The system uses feedback from multiple features (location consistency, time validity, movement pattern plausibility) to validate color-based matches. Even when the color resemblance threshold is set low to increase coverage, the feedback from other features filters out false positives, maintaining monitoring accuracy while maximizing tracking coverage.
Data Source
AI summary
Monitoring technique includes identifying objects in images and associating color characteristics with each identified object. When the color characteristics of an object in one image are above a color resemblance threshold to the color characteristics of an object in another image, the object in both images is considered the same. Otherwise, the respective object in both images are considered to be different. Data about time that each image including an identified object was obtained and location of each object when each image including an identified object was obtained is derived. An information conveyance system is activated to convey the time and location-related data about identified objects relative to defined areas of the site or movement of the identified objects or people into, between or out of the defined areas of the site or a communication resulting from such data satisfying one or more conditions.