Mesh Camera Networks for Real-Time 3D Dynamic Object Tracking
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Solution Overview
Problem
Existing camera systems struggle to effectively track dynamic objects in large, three-dimensional regions of space, making it challenging to detect and classify moving objects accurately.
Innovation Solution
A mesh network of nodes coupled to cameras, each with a computing device, processes image data to track objects in real-time, exchanging information to detect, track, and classify dynamic objects over large 3D spaces, generating maneuvering commands for local aircraft.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If a single ground-based camera system is used to track objects, then the system structure is simple, but the ability to detect and track dynamic objects in large 3D regions is insufficient
Solution Approach 1:
The system divides the large 3D monitoring space into multiple overlapping fields of view, with each camera responsible for a specific region. Multiple camera systems work in parallel to collectively cover the entire large volume, solving the contradiction between coverage volume and system complexity by distributing the monitoring task across multiple independent units.
Solution Approach 2:
The system transitions from 2D image plane tracking to 3D spatial tracking by using multiple cameras at different positions and angles. Through stereo vision and multi-view geometry, the system reconstructs object positions and trajectories in three-dimensional space, enabling effective tracking throughout the entire volumetric region.
2Measurement precision
If multiple cameras are used to cover large 3D regions, then the detection capability improves, but the data processing and coordination complexity increases
Solution Approach 1:
The system merges data from multiple cameras through centralized or distributed processing. By combining detections from different viewpoints and using data association algorithms, the system achieves more accurate object tracking while managing processing complexity through efficient data fusion techniques.
Solution Approach 2:
The system implements feedback mechanisms where detection results from one camera inform the search and tracking processes of other cameras. This coordinated feedback loop improves detection accuracy across the network while reducing redundant processing by sharing information about detected objects and their trajectories.
Data Source
AI summary
Methods and systems for detecting dynamic objects using a mesh network of nodes coupled to cameras are disclosed. The system can receive sequences of frames captured by a first capture device having a first pose and a second capture device having a second pose, and track objects over time across the first sequence of frames and the second sequence of frames. The system can map the objects to three-dimensional (3D) positions in a 3D coordinate space based on correspondences between the indications of the objects. The system can determine a 3D displacement of a subset of the 3D points, and generate a 3D volume surrounding the subset. The system can use the 3D volume to classify and predict a trajectory for the dynamic object, as well as determine a risk the dynamic object poses to a protected volume in the 3D coordinate space.