Sensor Network Local Classifiers for Object Tracking
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Solution Overview
Problem
Existing surveillance systems face challenges in efficiently tracking moving objects across complex monitoring areas due to the large volume of data required to be transmitted among numerous network nodes, making it difficult to identify and follow objects across multiple sub-areas effectively.
Innovation Solution
The system employs local classifiers trained on relevant objects within each sub-area, transmitting these classifiers instead of object features, allowing for efficient recognition and tracking by limiting the data transmission to only necessary subsets and enabling seamless object identification across adjacent sub-areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object information is transmitted from one network node to another network node for tracking moving objects across sub-areas, then tracking capability is improved, but data transmission complexity and volume increase significantly
Solution Approach 1:
The patent extracts only the essential classification information needed for tracking from the complete object data set. Instead of transmitting full object information including all features and attributes, the system transmits only the classifier identifiers and essential tracking parameters, significantly reducing data volume while maintaining tracking capability.
Solution Approach 2:
The patent segments the monitoring area into multiple sub-areas, each handled by a local network node with its own trained classifier. This segmentation allows each node to independently process objects in its local area and only exchange necessary information with adjacent nodes, reducing the overall data transmission complexity of the system.
2Measurement precision
If all moving object information is transmitted across the network for comprehensive tracking, then tracking accuracy is improved, but transmission time and processing load increase
Solution Approach 1:
The system extracts and transmits only the minimum necessary information for accurate tracking - specifically classifier identifiers and essential object parameters - rather than transmitting complete object data. This extraction approach maintains tracking accuracy while dramatically reducing transmission time and processing load.
Solution Approach 2:
The patent implements preliminary action by training local classifiers at each network node in advance. These pre-trained classifiers enable rapid local object recognition and classification, so that when objects need to be tracked across node boundaries, only the classification results need to be exchanged, not the raw data, thus reducing transmission time.
3Productivity
If local classifiers are trained for each network node to recognize relevant objects in their sub-area, then recognition efficiency is improved, but system adaptability to new objects decreases
Solution Approach 1:
The patent implements universality by designing a standardized classifier structure and information exchange protocol that can be applied across all network nodes regardless of their specific sub-area. The classifier framework is universal and can be trained for different object types and environments, allowing the system to maintain high recognition efficiency while adapting to new objects and scenarios through retraining rather than requiring fundamental system changes.
Data Source
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Figure 3
AI summary
The system (1) has a set of network nodes (2) assigned to a partial area of a monitored area, where one of the network nodes has a classification generator. The classification generator is programmably formed to train a local classifier for discrimination of a moving object that is relevant to the partial region. One network node is designed to pass object information e.g. object color, of the moving object to other network nodes whose adjacent partial regions are assigned in the monitored area. The object information is formed as the local classifier. Independent claims are also included for the following: (1) a transmission protocol for transmitting object information from one network node of a sensor network system (2) a method for recognizing an object (3) a computer program with a program code for executing a method for recognizing an object.