Real-Time Deep Learning for Moving Target Detection and Tracking Using Unmanned Aerial Vehicle

Doukhi, Oualid; Hossain, Sabir; Lee, Deok Jin

doi:10.5302/j.icros.2020.20.0027

Cited by 7 publications

(4 citation statements)

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“…On the premise of maintaining the speed advantage, the detection accuracy is further improved, especially the detection ability for small targets is strengthened. Also, the detection effect of high-coverage images is significantly higher than that of YOLOv2 [16].…”

Section: Target Detection Based On Improved Yolov3mentioning

confidence: 91%

Target Detection of Low-Altitude UAV Based on Improved YOLOv3 Network

Zhai

Zhang

2022

Journal of Robotics

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Most existing methods are difficult to detect low-altitude and fast-moving drones. A low-altitude unmanned aerial vehicle (UAV) target detection method based on an improved YOLOv3 network is proposed. While keeping the basic framework of the original model unchanged, the YOLOv3 model is improved. That is, multiscale prediction is added to enhance the detection ability of small-target objects. In addition, the two-axis Pan/Tilt/Zoom (PTZ) camera is controlled based on proportional integral derivative (PID), so that the target tends to the center of the field of view. It is more conducive to accurate detection. Finally, experiments are carried out using real UAV datasets. The results show that the mean average precision (mAP), AP50, and AP75 are 25.12%, 39.75%, and 26.03%, respectively, which are better than other methods. Also, the frame rate is 21 frames·s−1, which meets the performance requirements.

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Section: Target Detection Based On Improved Yolov3mentioning

confidence: 91%

Target Detection of Low-Altitude UAV Based on Improved YOLOv3 Network

Zhai

Zhang

2022

Journal of Robotics

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“…Li Zhihao et al, "presented an improved resampling particle filter algorithm in a moving target tracking and detection algorithm [20]. Meanwhile, Doksy et al, "combine depth research learning with Yolov to realize visual moving object detection [21]. What's more, the research of Xu Pengfei et al, "reveals that the moving target monitoring and intelligent tracking algorithm in video surveillance can achieve target tracking recognition [22].…”

Section: Related Workmentioning

confidence: 99%

Research on detecting moving targets with an improved Kalman filter algorithm

2023

KSII TIIS

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As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

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“…In order to improve the accuracy of moving target detection in UAV recognition, Li et al proposed an extraction method of cross-features [9]. Doukhi et al's research showed that the YOLO algorithm and deep learning can realize the visual moving target detection of UAV [10]. Taking the UAV as the carrier, moving target detection is effectively carried out through techniques such as image feature processing, but the use of the algorithm is not optimal.…”

Section: Related Workmentioning

confidence: 99%

“…In formula (10), C k represents a state transition matrix and G k represents a state input matrix. r represents the sensing model:…”

Section: Kalman Filter Algorithmmentioning

confidence: 99%

Moving Target Detection Technology Based on UAV Vision

Cheng¹,

Qin²,

Chen³

et al. 2022

Wireless Communications and Mobile Computing

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The detection of moving objects by machine vision is a hot research direction in recent years. It is widely used in military, medical, transportation, and agriculture. With the rapid development of UAV technology, as well as the high mobility of UAVs and the wide range of high-altitude vision, the target detection technology based on UAV vision is applied to traffic management such as vehicle tracking and detection of vehicle violations. The moving target detection technology in this study is based on the YOLOv3 algorithm. It implements moving vehicle tracking by means of Mean-Shift and Kalman filtering. In this paper, the Gaussian background difference technology is used to analyze the illegal behavior of the vehicle, and the color feature extraction technology is used to identify and locate the license plate, and the information of the illegal vehicle is entered into the database. The experiment compares the moving target detection of UAV vision and the traditional target detection in four aspects: recognition accuracy, recognition speed, manual time, and divergent results. The results show that the average accuracy rates of UAV vision-based moving target detection and traditional pattern recognition are 98.4% and 87.8%, respectively. The recognition speeds are 24.9 (vehicles/sec) and 10.6 (vehicles/sec), respectively. However, the artificial time and divergence results of moving target detection based on UAV vision are only 1/3 of the traditional mode. The moving target detection based on UAV vision has a better moving target detection ability.

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Real-Time Deep Learning for Moving Target Detection and Tracking Using Unmanned Aerial Vehicle

Cited by 7 publications

References 0 publications

Target Detection of Low-Altitude UAV Based on Improved YOLOv3 Network

Target Detection of Low-Altitude UAV Based on Improved YOLOv3 Network

Research on detecting moving targets with an improved Kalman filter algorithm

Moving Target Detection Technology Based on UAV Vision

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