Object Tracking via Homography and Contour Dilation
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Solution Overview
Problem
Existing object detection and tracking systems face challenges in efficiently processing images from multiple cameras in large physical spaces, such as stores, to identify and track people and objects in real-time, especially when dealing with complex environments and multiple objects, and lack the ability to accurately determine physical locations and handle handoffs between camera views.
Innovation Solution
The system generates homographies to map pixel locations from cameras to physical locations in a global plane, enabling accurate position tracking, handoff of tracking information between sensors, detection of shelf interactions, and association of items with individuals using virtual curtains and predefined zones, while employing contour dilation and machine learning approaches for efficient object re-identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image processing techniques are used to identify objects in busy environments, then object identification can be achieved, but the process becomes computationally intensive and time-consuming
Solution Approach 1:
The system segments the tracking process into distinct stages: contour detection, contour dilation, feature identification, and tracking. By dividing the complex task of object identification and tracking into smaller, manageable segments, the system reduces computational complexity at each stage while maintaining overall accuracy. The contour dilation technique specifically segments the object boundary detection from the internal feature analysis.
Solution Approach 2:
The system performs preliminary contour dilation on detected objects before detailed feature analysis. This preliminary action of expanding contours creates a buffer zone that simplifies subsequent tracking by pre-identifying potential object boundaries and reducing the computational load during real-time tracking operations.
2Area of stationary object
If multiple cameras are deployed to cover large physical spaces, then comprehensive object tracking is enabled, but the complexity of processing and combining information from multiple cameras increases
Solution Approach 1:
The system merges tracking information from multiple cameras by maintaining a global contour representation that integrates data from different camera views. The contour dilation technique provides a unified approach that works consistently across multiple cameras, allowing seamless combination of tracking data without requiring complex camera-specific processing for each view.
Solution Approach 2:
The contour dilation technique serves as a universal method that can be applied to objects detected by any camera in the system. This multi-functional approach allows the same processing algorithm to handle objects in different camera views, different orientations, and different scales, reducing the need for view-specific processing logic.
3Reliability
If traditional object tracking methods are used, then basic tracking can be achieved, but the ability to determine physical locations and handle handoffs between camera views is lacking
Solution Approach 1:
The system uses dilated contours as an intermediary representation that bridges the gap between different camera views. These expanded contours serve as mediators that facilitate smooth handoffs between cameras by creating overlapping zones where objects can be tracked continuously as they move between camera fields of view, enabling accurate physical location determination.
Data Source
AI summary
A system includes a first sensor configured to generate images of at least a first portion of a space. A processor of the system is configured to determine a position of a possible object in the space based on generated images.