Human-Automation Collaborative Object Tracking System
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Solution Overview
Problem
Current object tracking systems in Remotely Piloted Aircraft (RPA) Intelligence, Surveillance, and Reconnaissance (ISR) missions face challenges in maintaining visual lock on moving vehicles due to limited field of view, occlusions, and varying image quality, leading to loss of tracking and reliance on human observers for continuous attention.
Innovation Solution
A method and system for efficient human collaboration with automated object tracking that fuses spatially registered features, motion data, and trajectory prediction to maintain tracking, providing confidence indicators and fault diagnostics through a user interface, enabling dynamic collaboration and reducing human workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated object tracking is used to reduce human workload, then productivity is improved, but reliability deteriorates due to tracking loss from occlusions and limited field of view
Solution Approach 1:
The system implements a confidence indicator that provides continuous feedback to the human operator about the reliability of automated tracking. When confidence drops below a threshold, the system alerts the operator to take over, preventing complete tracking failure while maintaining automated operation during high-confidence periods.
Solution Approach 2:
The system dynamically adjusts the level of human involvement based on tracking confidence. During high-confidence periods, the system operates fully automatically; during low-confidence periods, it transitions to human-controlled operation, creating a flexible human-automation collaboration model that optimizes both productivity and reliability.
2Reliability
If human observers continuously monitor tracking to ensure reliability, then tracking reliability is improved, but productivity deteriorates due to human fatigue and limited attention
Solution Approach 1:
The automated tracking system performs self-monitoring by calculating confidence indicators based on its own performance metrics. This self-assessment capability allows the system to identify when it needs human intervention without requiring constant human supervision, reducing the cognitive load on operators.
Solution Approach 2:
The system provides automated feedback about tracking quality and confidence levels, enabling operators to focus on high-level decisions rather than continuous monitoring. This feedback mechanism allows sustained operation with reduced human attention requirements.
3Reliability
If multiple tracking methods are fused to improve tracking reliability, then reliability is improved, but device complexity increases
Solution Approach 1:
The system combines multiple tracking methods (feature-based tracking, motion-based tracking, and template matching) into a unified framework. By merging these approaches and fusing their results, the system achieves more reliable tracking than any single method could provide alone, while managing complexity through integrated processing.
Data Source
AI summary
A system includes a control station that enables efficient human collaboration with automated object tracking. The control station is communicatively coupled to an aerial vehicle to receive full motion video of a ground scene taken by an airborne sensor of the aerial vehicle. The control station spatially registers features of a movable object present in the ground scene and determines motion of the movable object relative to the ground scene. The control station predicts a trajectory of the movable objective relative to the ground scene. The control station tracks the movable object based on data fusion of: (i) the spatially registered features; (ii) the determined motion; and (iii) the predicted trajectory of the movable object. The control station presents a tracking annotation and a determined confidence indicator for the tracking annotation on a user interface device to facilitate human collaboration with object tracking.