UAV Visual Tracking With Local Re-Identification and FFT Correlation

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Solution Overview

Problem

Existing consumer UAVs face challenges in implementing high-performance visual tracking without GPS, as current solutions require powerful computational platforms, making them unsuitable for low-end consumer UAVs.

Innovation Solution

A real-time visual object tracking system that uses correlation maps computed between image patches within search windows, employing feature extraction and Fast Fourier Transform to determine the updated location of a target object, allowing for low-complexity and high-performance tracking on low-end platforms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If high-complexity visual tracking functionalities are implemented, then tracking performance is improved, but device complexity increases requiring powerful computational platforms

Engineering Contradiction:

Improvetracking performanceVSAvoidcomputational platform complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the visual tracking task into distinct modules: feature extraction, correlation map computation, and target location determination. This modular approach allows each component to be optimized independently and executed efficiently on resource-constrained embedded platforms, resolving the contradiction between tracking performance and device complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent uses template matching where a reference image patch (template) is copied and compared against regions in the target video frame. This copying approach enables robust visual tracking without requiring complex machine learning models, achieving reliable tracking performance on low-end devices

Inventive Principle:

Principle #26Copying

2Adaptability or versatility

If real-time visual tracking is implemented without GPS, then tracking capability is improved for GPS-denied environments, but computational requirements increase

Engineering Contradiction:

Improvetracking capability in GPS-denied environmentsVSAvoidcomputational energy consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent replaces GPS-based mechanical positioning with vision-based optical tracking. By substituting the GPS mechanical system with a camera and image processing algorithm, the system achieves adaptability in GPS-denied environments while maintaining reasonable energy consumption through efficient correlation computations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If feature extraction and correlation map computation are used, then tracking accuracy is improved, but processing time increases

Engineering Contradiction:

Improvetracking accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements tracking at periodic intervals rather than continuously processing every video frame. By selecting key frames for feature extraction and correlation computation, the system maintains high tracking accuracy while significantly reducing overall processing time and energy consumption

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The patent applies correlation map computation only to regions of interest within video frames rather than processing the entire frame. This partial action approach maintains tracking accuracy in the target region while reducing computational load and processing time

Inventive Principle:

Principle #16Partial or excessive action

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

Enables effective visual tracking of moving objects without GPS, achieving real-time performance on low-end UAVs by simplifying the tracking process through feature-based correlation methods, thus expanding the applicability of visual tracking capabilities.

Implementation Method 1

employing feature extraction and Fast Fourier Transform to determine the updated location of a target object

Methodology Applied

Scientific EffectFast Fourier Transform:

Data Source

PatentUS11645765B2Real-time visual object tracking for unmanned aerial vehicles (UAVs)

Publication Date: 2023.05.09 ALTUMVIEW SYST INC

AI summary

Embodiments described herein provide various examples of real-time visual object tracking. In another aspect, a process for performing a local re-identification of a target object which was earlier detected in a video but later lost when tracking the target object is disclosed. This process begins by receiving a current video frame of the video and a predicted location of the target object. The process then places a current search window in the current video frame centered on or in the vicinity of the predicted location of the target object. Next, the process extracts a feature map from an image patch within the current search window. The process further retrieves a set of stored feature maps computed at a set of previously-determined locations of the target object from a set of previously-processed video frames in the video. The process next computes a set of correlation maps between the feature map and each of the set of stored feature maps. The process then attempts to re-identify the target object locally in the current video frame based on the set of computed correlation maps.