hTLD: A Human-in-the-loop Target Detection and Tracking Method for UAV

Zhu, Yuting; Wang, Chang; Niu, Yifeng; Wu, Lizhen

doi:10.1109/gncc42960.2018.9018702

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…Refer to our previous work for details of hTLD (Zhu et al, 2018). However, the human operator is fully responsible for the judgement of a tracking failure in hTLD.…”

Section: The Supervisory Target Tracking Frameworkmentioning

confidence: 99%

“…Considering that there exists errors between the estimated location and the true location of the target (see Figure 3), we use a bigger bounding box with the size of nW×nH pixels to cover the target, where n>1. As the size of the bounding box influences the tracking result, the bounding box should be as accurate as possible to exclude irrelevant features (Zhu et al, 2018). Based on the bounding box obtained from the eye-tracker, we propose an adaptive algorithm to separate the target from the background.…”

Section: Use Eye-tracker To Select Target For Learningmentioning

confidence: 99%

“…In our previous work, hTLD (human-in-the-loop TLD) (Zhu et al, 2018) includes human into the loop of the original TLD algorithm, which supports on-line learning of the new appearances of the target of interest. However, monitoring several video streams simultaneously remains a challenge for a human operator assisted by hTLD.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with our previous work (Zhu et al, 2018), the main contributions of this paper are as follows:…”

Section: Introductionmentioning

confidence: 99%

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Multi-video Supervisory Target Tracking Improved by Interactive On-line Learning

Wang

Zhu

Wen

et al. 2018

Electronic Workshops in Computing

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It is difficult for a human operator to monitor multiple video streams in real-time surveillance tasks. A variety of machine learning algorithms have been proposed for target tracking, but human intervention is still necessary because the algorithms have limitations to detect and recover from tracking failures. In this paper, we propose a supervisory target tracking method for multi-video surveillance tasks based on hTLD (human-in-the-loop Tracking-Learning-Detection). The human operator can monitor both the original video streams and the calculated confidence values of the tracking results. Human intervention allows to update the training data for TLD whenever a tracking failure is observed, by manually drawing a bounding box or using an eye-tracker to relocate the target. Meanwhile, an interactive on-line learning algorithm is used to learn the relations between the confidence values and the tracking results. In this way, the confidence values help avoid unnecessary intervention caused by false alarms of tracking failures. Experimental results have demonstrated that the proposed method can reduce the task completion time. Interactive learning, eye-tracker, target tracking, TLD.

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“…Refer to our previous work for details of hTLD (Zhu et al, 2018). However, the human operator is fully responsible for the judgement of a tracking failure in hTLD.…”

Section: The Supervisory Target Tracking Frameworkmentioning

confidence: 99%

Section: Use Eye-tracker To Select Target For Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Compared with our previous work (Zhu et al, 2018), the main contributions of this paper are as follows:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-video Supervisory Target Tracking Improved by Interactive On-line Learning

Wang

Zhu

Wen

et al. 2018

Electronic Workshops in Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…These challenges include sensitivity to varying lighting conditions, angles, and obstructions, and difficulties in processing complex backgrounds. While effective in simpler environments, these methods often lead to false positives and missed detections in more intricate scenarios [5][6][7]. Conversely, the advent of deep learning, especially convolutional neural networks (CNNs), has markedly improved the detection of small-scale objects in UAV imagery, addressing many limitations inherent in traditional techniques [8,9].…”

Section: Introductionmentioning

confidence: 99%

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones

Kucukayan,

Karacan

2024

Sensors

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In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as “You Only Look Once” (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model’s accuracy and the reliability of indoor human detection in real-time drone applications.

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Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Wang

Yan

et al. 2020

Chinese Journal of Aeronautics

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hTLD: A Human-in-the-loop Target Detection and Tracking Method for UAV

Cited by 4 publications

References 10 publications

Multi-video Supervisory Target Tracking Improved by Interactive On-line Learning

Multi-video Supervisory Target Tracking Improved by Interactive On-line Learning

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

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