Object Tracking Across Non-Overlapping Sensors via Automatic Learning
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Solution Overview
Problem
Existing video surveillance systems with non-overlapping sensors face challenges in tracking objects across multiple cameras without overlapping fields of view, requiring manual labeling and complex network structures, which are costly and inefficient.
Innovation Solution
An object tracking method and apparatus that uses an automatic learning approach to estimate characteristic functions such as sensor spatial relation, time difference of movement, and similarity in appearance, allowing for object tracking across non-overlapping sensors without manual intervention or knowledge of sensor deployment blueprints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling and complex network structures are used for object tracking across non-overlapping sensors, then tracking capability is achieved, but system cost and complexity increase
Solution Approach 1:
The system performs automatic self-calibration by autonomously learning sensor spatial relationships, object appearance characteristics, and movement patterns from video data without requiring manual labeling. The calibration module automatically adjusts parameters and establishes tracking rules, eliminating the need for manual intervention and reducing system complexity while maintaining reliable cross-sensor object tracking
Solution Approach 2:
The system performs preliminary automatic calibration during a setup phase to pre-establish sensor spatial relationships and object characteristics before actual tracking begins. This preliminary action creates a foundation of learned parameters and tracking rules that enable efficient real-time object tracking across non-overlapping sensors without requiring complex manual configuration during operation
2Area of stationary object
If the number of cameras increases to cover larger areas, then surveillance coverage is improved, but color correction and network structure become complicated
Solution Approach 1:
When new cameras are added to expand surveillance coverage, the system automatically performs self-calibration to learn the spatial relationships between existing and new sensors. The calibration module autonomously adjusts color correction parameters and establishes tracking rules for the expanded network, eliminating the need for complex manual configuration and integration when scaling the system
3Ease of operation
If automatic learning method is used to estimate characteristic functions, then manual labeling is eliminated, but computational complexity increases
Solution Approach 1:
The system replaces manual mechanical labeling operations with automatic computational learning methods. The calibration module uses machine learning algorithms to automatically estimate characteristic functions from video data, substituting human manual work with automated computational processes that learn sensor spatial relations, object appearances, and movement patterns without requiring manual intervention
Data Source
AI summary
An object tracking method for a non-overlapping-sensor network works in a sensor network. The method may comprise a training phase and a detection phase. In the training phase, a plurality of sensor information measured by the sensors in the sensor network is used as training samples. At least an entrance/exit is marked out within the measurement range of each sensor. At least three characteristic functions including sensor spatial relation among the sensors in the sensor network, difference of movement time and similarity in appearance, are estimated by an automatically learning method. The at least three characteristic functions are used as the principles for object tracking and relationship linking in the detection phase.