Tracking Moving Objects Using Predictive Location Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tracking systems face challenges in accurately tracking moving objects in real-time due to time delays in image processing and communication, leading to instability and potential mis-locking on background objects, especially when objects move out of the field of view or require precise location estimation.
Innovation Solution
A method and system that generate, store, and transmit a succession of images, allowing for the selection of an object of interest and the use of object data to predict and lock onto the object's real-time location, even if it is not visible in the latest image, by estimating its location based on past image data and generating direction commands for the sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing and communication are performed in real-time, then tracking accuracy is improved, but time delay increases
Solution Approach 1:
The system performs preliminary actions by storing multiple successive images and predicting object location before the tracking command is fully processed. The predictor estimates where the object will be when the sensor arrives, allowing the system to prepare tracking data in advance rather than waiting for real-time processing to complete.
Solution Approach 2:
The system dynamically adjusts the tracking process by using motion parameters from multiple images to predict object movement. Instead of static real-time processing, the system continuously updates predictions based on changing object position, velocity, and acceleration derived from successive image frames.
2Stability of the object's composition
If the sensor continuously tracks the object, then tracking stability is improved, but the system may mis-lock on background objects due to time delay
Solution Approach 1:
The system uses feedback by comparing predicted object locations with actual sensor data from successive images. The predictor continuously refines its estimates of object position and motion parameters based on feedback from multiple image frames, allowing the system to distinguish the true object from background elements even when time delays occur.
Solution Approach 2:
By calculating predicted object location and motion parameters in advance using multiple successive images, the system prepares accurate tracking information before the sensor arrives. This preliminary calculation ensures the sensor can reliably lock onto the correct object rather than background elements.
3Measurement precision
If multiple images are stored and processed, then object location prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system extracts only the essential motion parameters (position, velocity, acceleration) from multiple successive images rather than processing entire image datasets. By taking out and storing only these critical parameters, the system achieves accurate location prediction while minimizing memory requirements and processing complexity.
Data Source
AI summary
A method for tracking an object that is embedded within images of a scene, including: in a sensor unit, generating, storing and transmitting over a communication link a succession of images of a scene. In a remote control unit, receiving the images, receiving a command for selecting an object of interest in a given image and determining object data associated with the object and transmitting the object data to the sensor unit. In the sensor unit, identifying the given image and the object of interest using the object data, and tracking the object in other images. If the object cannot be located in the latest image of the stored succession of images, using information of images in which the object was located to predict estimated real-time location thereof and generating direction commands to the movable sensor for generating realtime images of the scene and locking on the object.