Volumetric Object Tracking Using Depth Sensors and AI
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Solution Overview
Problem
Current object tracking mechanisms are costly, inaccurate, and computationally expensive, and lack the ability to provide unique identification and annotation of moving objects with metadata.
Innovation Solution
The use of a volumetric sensor, such as a depth sensor, in conjunction with other sensors like video cameras, microphones, and thermal sensors, to calculate a volumetric representation of objects and assign a unique volumetric profile with metadata, which includes attributes like position and centroid, for accurate tracking and annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video cameras and traditional tracking systems are used, then object tracking is achieved, but the system becomes costly and computationally expensive
Solution Approach 1:
The patent replaces traditional video camera-based tracking systems with a depth sensor-based volumetric tracking system. The depth sensor captures spatial information directly in 3D space, eliminating the need for complex computer vision algorithms to interpret 2D video feeds. This substitution reduces computational requirements and system cost while maintaining tracking accuracy through direct volumetric measurement of object positions and movements.
Solution Approach 2:
The patent changes the fundamental parameter of measurement from 2D image coordinates to 3D volumetric coordinates. By using depth sensors to directly measure distance and spatial position, the system transforms the tracking parameter space from planar to volumetric, enabling more accurate and computationally efficient object tracking without requiring expensive infrastructure.
2Loss of information
If traditional tracking systems are used, then object detection is achieved, but unique identification and metadata annotation are not provided
Solution Approach 1:
The patent segments the tracking system into distinct functional components: depth sensing for volumetric detection, machine learning models for identification, and metadata annotation systems for information enrichment. This segmentation allows each component to specialize in its function, providing comprehensive object identification and annotation capabilities while keeping the overall system manageable through modular architecture.
Solution Approach 2:
The patent introduces machine learning models and metadata systems as intermediary layers between the depth sensor and the tracking output. These intermediaries process the volumetric data to extract unique object identifiers and attach descriptive metadata, bridging the gap between raw sensor data and meaningful object information without requiring direct complex processing in the sensor itself.
3Measurement precision
If depth sensors and volumetric representations are used, then accurate object tracking is achieved, but computational processing increases
Solution Approach 1:
The patent applies partial processing by focusing computational resources only on detected objects of interest rather than processing the entire volumetric space. The system uses the depth sensor to quickly identify regions containing objects, then applies more intensive volumetric analysis only to those specific regions, reducing overall computational energy while maintaining high tracking precision for target objects.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides an inexpensive and accurate method for tracking and annotating moving objects, enabling efficient identification and metadata annotation, overcoming the limitations of prior art systems.
Implementation Method 1
time-of-flight sensors from which depth can be calculated
Data Source
AI summary
A system and method for tracking and annotating objects in a 3D model is disclosed. The method includes receiving a signal feed from a group of sensors including at least one depth sensor, determining a reference signal feed from the signal feed, determining a volumetric representation of at least one object not present in the reference signal feed, detecting at least one object, person or animal using pre-trained convolutional neural network models and assigning a unique volumetric profile to at least one object, and/or storing the unique volumetric profile in an object database. Such CNN can be custom-trained to detect specific objects, animals or persons. The signal feed may include at least one 3D area of interest. Also, the unique volumetric profile may include a unique volumetric id obtained from a group of attributes. The unique volumetric profile may include at least a unique volumetric position and a unique volumetric centroid. The group of sensors may further include video cameras, thermal, and chemical sensors.