Tracking Objects Across Disjoint Camera Fields Using Transition Parameters
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Solution Overview
Problem
Current methods fail to automatically track objects between disjoint camera fields of view, requiring significant human labor or manual input, especially in scenarios where camera fields do not overlap, such as in freeway monitoring or street surveillance with large gaps between camera views.
Innovation Solution
Determination of transition parameters like lane offset, speed correction, and variance, which are calculated by matching objects in one camera's field of view with corresponding objects in another, using kinematic values and image analysis to establish correspondence and enhance tracking across non-overlapping fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated multi-camera tracking methods using Bayesian frameworks and appearance models are employed, then object tracking between cameras is enabled, but the system complexity and computational requirements increase significantly
Solution Approach 1:
The patent segments the tracking problem into distinct components: transition parameter determination (spatial-temporal relationships between camera fields of view) and object matching (associating objects across cameras). This segmentation allows each component to be handled separately with dedicated algorithms, reducing overall system complexity while maintaining automation.
Solution Approach 2:
The patent performs preliminary determination of transition parameters between camera fields of view before actual object tracking occurs. By pre-calculating spatial-temporal relationships and storing them as transition parameters, the system avoids complex real-time calculations during tracking, thereby reducing computational complexity while preserving automated tracking capability.
2Measurement precision
If manual input and human labor are used to track objects between disjoint camera fields of view, then tracking accuracy can be maintained, but productivity and efficiency decrease
Solution Approach 1:
The patent enables the system to automatically determine transition parameters and perform object matching without human intervention. The system uses image data from multiple cameras, kinematic values, and pre-determined transition parameters to autonomously associate objects across disjoint fields of view, achieving both high accuracy and efficiency through self-service automation.
Solution Approach 2:
The patent incorporates feedback mechanisms where transition parameters are determined from observed object transitions and used to improve subsequent tracking accuracy. The system continuously refines its parameter estimates based on actual tracking performance, maintaining high accuracy while operating autonomously at high speed.
3Area of stationary object
If camera fields of view are positioned to cover large areas, then surveillance coverage is improved, but gaps between fields of view increase making object tracking difficult
Solution Approach 1:
The patent transitions from two-dimensional spatial overlap (traditional approach requiring overlapping camera views) to incorporating the time dimension through transition parameters that model object motion between disjoint fields of view. By adding temporal information and kinematic constraints, the system can reliably track objects across large coverage areas even when camera fields do not spatially overlap.
Solution Approach 2:
The patent changes the parameters used for tracking from purely spatial overlap-based methods to include transition parameters that encompass spatial-temporal relationships, kinematic values, and appearance characteristics. This parameter transformation enables reliable tracking across disjoint fields of view while maintaining large surveillance coverage areas.
4Measurement precision
If transition parameters are determined through extensive image analysis and matching, then association accuracy between objects improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary determination of transition parameters including spatial offsets, temporal delays, and kinematic relationships before actual object matching occurs. By pre-processing and storing these parameters, the system reduces the computational burden during real-time matching operations, achieving high accuracy associations with reduced processing time.
Solution Approach 2:
The patent uses a multi-stage matching approach where transition parameters first filter potential object associations, then appearance models and kinematic values provide refined matching. This partial action strategy processes only relevant object pairs through full analysis rather than all possible pairs, significantly reducing processing time while maintaining high association accuracy.
Data Source
AI summary
A method, system and non-transitory computer readable storage medium, the computer-implemented method comprising: receiving a first image captured by a first capture device, the first images depicting first objects; obtaining a kinematic value for each of the first objects; receiving a second image captured by a second capture device, the second images depicting second objects; and analyzing the first and second images and the kinematic value, to determine transition parameters related to transition of objects from a first field of view of the first capture device to a second field of view of the second capture device, wherein the first capture device and the second capture device are calibrated, wherein a gap exists between the first and the second fields of view, and wherein said transition parameters are usable for associating further objects captured by the first capture device with corresponding objects captured by the second capture device.