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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sun, Rong Chai, Guangxin Jiangxi Science and Technology Normal University Jiangxi330038 China

ISBN: (纸本)9798350383348

With the rapid development of video surveillance technology, the detection of moving objects in video has become a hot issue in the field of computer vision. In Wushu video, the detection of moving objects is of great significance for analyzing athletes' movements, improving sports training effect and realizing automatic scoring. This paper will focus on the methods and technologies of moving object detection in Wushu ring video. © 2024 IEEE.

关键词： artificial intelligence theory detection and tracking Intelligent tracking martial arts ring Movement goals Robocup

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Maritime Target Radar detection and tracking via DTNet Transfer Learning Using Multi-Frame Images

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REMOTE SENSING 2025年第5期17卷 836-836页

作者： He, Xiaoyang Chen, Xiaolong Du, Xiaolin Wang, Xinghai Xu, Shuwen Guan, Jian Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Naval Aviat Univ Marine Target Detect Res Grp Yantai 264001 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

Traditional detection and tracking methods struggle with the complex and dynamic maritime environment due to their poor generalization capabilities. To address this, this paper improves the YOLOv5 network by integrating Transformer and a Convolutional Block Attention Module (CBAM) with the multi-frame image information obtained from radar scans. It proposes a detection and tracking method based on the detection tracking Network (DTNet), which leverages transfer learning and the DeepSORT tracking algorithm, enhancing the detection capabilities of the model across various maritime environments. First, radar echoes are preprocessed to create a dataset of Plan Position Indicator (PPI) images for different marine conditions. An integrated network for detecting and tracking maritime targets is then designed, utilizing the feature differences between moving targets and sea clutter, along with the coherence of inter-frame information for moving targets, to achieve multi-target detection and tracking. The proposed method was validated on real maritime targets, achieving a precision of 99.06%, which is a 7.36 percentage point improvement over the original YOLOv5, demonstrating superior detection and tracking performance. Additionally, the impact of maritime regions and weather conditions is discussed, showing that, when transferring from Region I to Regions II and III, the precision reached 92.2% and 89%, respectively, and, when facing rainy weather, although there was interference from the sea clutter and rain clutter, the precision was still able to reach 82.4%, indicating strong generalization capabilities compared to the original YOLOv5 network.

关键词： maritime moving targets detection and tracking radar images deep learning inter-frame information

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detection and tracking METHOD OF SMALL-SIZED UAV BASED ON YOLOV5 19

DETECTION AND TRACKING METHOD OF SMALL-SIZED UAV BASED ON YO...

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19th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： You Jiang Gu Jingliang Zhou Yanqing Wan Min Wang Jianwei China Acad Engn Phys Inst Appl Elect Mianyang 621900 Sichuan Peoples R China China Acad Engn Phys Grad Sch Beijing 100088 Peoples R China

ISBN: (纸本)9781665493895

In recent years, accidents such as casualties and economic losses have occurred due to unmanned aerial vehicles (UAVs) flying in violation of regulations. The monitoring and countermeasures of UAVs with low flying altitude, slow flying speed and small size have become the current research hotspot. Vision-based methods are still one of the most mainstream methods, but due to the characteristics of UAVs such as small size, large attitude changes, and low flight altitudes, there are difficulties such as poor imaging contrast, complex background, and small proportion of targets. Aiming at the above difficulties, this paper proposes an improved YOLOv5 UAV detection algorithm and tracking method. The detection probability of drones is improved by adding a detection heads and attention module, and high-speed tracking performance is achieved by training a low-resolution detector combined with the Kalman algorithm. This paper deploys this method on NVIDIA Jetson Xavier NX, resulting in an output rate of 200 FPS.

关键词： Anti-UAVs YOLOv5 detection and tracking Jetson Xavier NX

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VCSEL-based photonic spiking neural networks for ultrafast detection and tracking

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NEUROMORPHIC COMPUTING AND ENGINEERING 2024年第1期4卷 014010-014010页

作者： Robertson, Joshua Kirkland, Paul Di Caterina, Gaetano Hurtado, Antonio Univ Strathclyde Inst Photon SUPA Dept Phys Glasgow Scotland Univ Strathclyde Dept Elect & Elect Engn Glasgow Scotland

Inspired by efficient biological spike-based neural networks, we demonstrate for the first time the detection and tracking of target patterns in image and video inputs at high-speed rates with networks of multiple artificial spiking optical neurons. Using photonic systems of in-parallel spiking vertical cavity surface emitting lasers (VCSELs), we demonstrate the implementation of multiple convolutional kernel operators which, in combination with optical spike signalling, enable the detection and tracking of target features in images/video feeds at an ultrafast photonic operation speed of 1 ns per pixel. Alongside a single layer optical spiking neural network (SNN) demonstration, a multi-layer network of photonic (GHz-rate) spike-firing neurons is reported where the photonic system successfully tracks a large complex feature (Handwritten Digit 3). The consecutive photonic layers perform spike-enabled image reduction and convolution operations, and interact with a software-implemented SNN, that learns the feature patterns that best identify the target to provide a high detection efficiency even in the presence of a distractor feature. This work therefore highlights the effectiveness of combining neuromorphic photonic hardware and software SNNs, for efficient learning and ultrafast operation, thanks to the use of spiking light signals, towards tackling complex AI and computer vision problems.

关键词： neuromorphic computing semiconductor laser spiking neural networks image processing detection and tracking

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A selective region-based detection and tracking approach towards the recognition of dynamic bare hand gesture using deep neural network

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MULTIMEDIA SYSTEMS 2022年第3期28卷 861-879页

作者： Yadav, Kuldeep Singh Anish Monsley, K. Laskar, Rabul Hussain Misra, Songhita Bhuyan, M. K. Khan, Taimoor NIT Silchar Elect & Commun Engn Silchar 788010 Assam India NIT Andhra Pradesh Elect & Commun Engn Tadepalligudem 534101 Andhra Pradesh India IIT Guwahati Elect & Elect Engn Gauhati 781039 Assam India

Complexities such as variations in pose, scale, speed, illumination, occlusion, etc., in detecting the gesture object at the first frame and tracking it in a dynamic environment make it challenging for gesture-based human-computer interactions. The kernel-based operation on the first frame requires additional computational resources for detecting the bare hand. Therefore, a region-based detection approach is proposed to detect the bare hand-like regions using skin color and movement information. Using transfer learning, the AlexNet is used for detecting the bare hand from this region of interest. To track it in the successive frames, a detection and tracking (DaT) algorithm is proposed. The smoothen trajectory is utilized for training a separate deep neural network to recognize 60 isolated dynamic gestures. An average accuracy of 97.80%, 96.20% is achieved for the bare hand detection on the Oxford hand database, NITS hand gesture VIII database. The experimental observation reveals that the proposed method is computationally efficient as compared to the existing models. For the recognition of 60 gestures, an average accuracy of 96.48% is achieved. The proposed system provides a relative improvement of 17% over the base models.

关键词： ROI detection detection and tracking AlexNet Dynamic hand gesture recognition Isolated gesture Gesture object detection

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A Model-Free Moving Object detection and tracking Framework Based on UAV Data

A Model-Free Moving Object Detection and Tracking Framework ...

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International Conference on Autonomous Unmanned Systems (ICAUS)

作者： Wang, Ziyin Li, Dongdong Kuai, Yangliu Sun, Yuxin China Elect Technol Grp Corp Res Inst 54 Shijiazhuang 050011 Hebei Peoples R China Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Hunan Peoples R China Sci & Technol Electroopt Informat Secur Control L Tianjin 300000 Peoples R China

ISBN: (纸本)9789819904815;9789819904792;9789819904785

At present, object detection and target tracking play an important role in the development of unmanned aerial vehicle(UAV) applications. The detection and tracking algorithms based on deep learning show excellent performance on aerial data. However, these algorithms, depending on GPU and high-quality datasets, are difficult to migrate to terminals with microcomputers. In this paper, we develop a tracking method combining DeepSort and Histogram of Oriented Gradient (HOG), named HOGSort within a model-free and non-deep learning framework for detection and tracking of moving objects. Moving objects are detected by inter-frame difference algorithms and tracked by HOGSort. Experiments based on aerial videos show that most trackers can continuously track their respective targets, proving that our framework is effective.

关键词： Aerial video Model-free Non-deep learning detection and tracking HOGSort

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detection and tracking of a Moving Target for UAV Based on Machine Vision 7

Detection and Tracking of a Moving Target for UAV Based on M...

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7th International Conference on Control, Automation and Robotics (ICCAR)

作者： Qi, Duo Li, Zhe Ren, Baoxiang Lei, Pengfei Yang, Xiaotian Air Force Engn Univ Air Traff Control & Nav Coll Aviat Swarm Technol & Operat Applicat Lab Xian Peoples R China Air Force Euipment Dept Representat Off 7 Xian Peoples R China

ISBN: (纸本)9781665449861

With the continuously development of the technology related to unmanned air vehicle (UAV), the automation and intelligent levels become higher and higher. In view of the actual needs of precise control and the specific requirements of accurate tracking competition, this paper designs and realizes a complete process of detection and tracking. First, the images of moving targets are collected by machine vision. Then, an improved Yolo V3 algorithm is applied to finish the target detection work and an achieved proportional integral derivative (PID) controller to track the moving target is designed. To verify the accuracy of the detection and tracking algorithm and the effectiveness of the complete process, a verification system is also built. Real flight test results show that the UAV can complete the task with a high dynamic target detection rate and accurate tracking rate.

关键词： indoor moving target detection and tracking machine vision PID controller

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Improved Particle Filter Algorithm for Multi-Target detection and tracking

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SENSORS 2024年第14期24卷 4708页

作者： Cheng, Yi Ren, Wenbo Xiu, Chunbo Li, Yiyang Tiangong Univ Sch Control Sci & Engn Tianjin 300387 Peoples R China

In modern radar detection systems, the particle filter technique has become one of the core algorithms for real-time target detection and tracking due to its good nonlinear and non-Gaussian system state estimation capability. However, when dealing with complex dynamic scenes, the traditional particle filter algorithm exposes obvious deficiencies. The main expression is that the sample degradation is serious, which leads to a decrease in estimation accuracy. In multi-target states, the algorithm is difficult to effectively distinguish and stably track each target, which increases the difficulty of state estimation. These problems limit the application potential of particle filter technology in multi-target complex environments, and there is an urgent need to develop a more advanced algorithmic framework to enhance its robustness and accuracy in complex scenes. Therefore, this paper proposes an improved particle filter algorithm for multi-target detection and tracking. Firstly, the particles are divided into tracking particles and searching particles. The tracking particles are used to maintain and update the trajectory information of the target, and the searching particles are used to identify and screen out multiple potential targets in the environment, to sufficiently improve the diversity of the particles. Secondly, the density-based spatial clustering of applications with noise is integrated into the resampling phase to improve the efficiency and accuracy of particle replication, so that the algorithm can effectively track multiple targets. Experimental result shows that the proposed algorithm can effectively improve the detection probability, and it has a lower root mean square error (RMSE) and a stronger adaptability to multi-target situation.

关键词： particle filter multi-target density-based clustering detection and tracking

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CacheTrack-YOLO: Real-Time detection and tracking for Thyroid Nodules and Surrounding Tissues in Ultrasound Videos

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第10期25卷 3812-3823页

作者： Wu, Xiangqiong Tan, Guanghua Zhu, Ningbo Chen, Zhilun Yang, Yan Wen, Huaxuan Li, Kenli Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Wenzhou Med Univ Affiliated Hosp 2 Dept Ultrason Diag Wenzhou 325088 Peoples R China Wenzhou Med Univ Yuying Childrens Hosp Wenzhou 325088 Peoples R China Southern Med Univ sity Affiliated Shenzhen Maternal & Child Healthcare Dept Ultrasound Shenzhen 518047 Peoples R China

To accurately detect and track the thyroid nodules in a video is a crucial step in the thyroid screening for identification of benign and malignant nodules in computer-aided diagnosis (CAD) systems. Most existing methods just perform excellent on static frames selected manually from ultrasound videos. However, manual acquisition is labor-intensive work. To make the thyroid screening process in a more natural way with less labor operations, we develop a well-designed framework suitable for practical applications for thyroid nodule detection in ultrasound videos. Particularly, in order to make full use of the characteristics of thyroid videos, we propose a novel post-processing approach, called Cache-Track, which exploits the contextual relation among video frames to propagate the detection results into adjacent frames to refine the detection results. Additionally, our method can not only detect and count thyroid nodules, but also track and monitor surrounding tissues, which can greatly reduce the labor work and achieve computer-aided diagnosis. Experimental results show that our method performs better in balancing accuracy and speed.

关键词： Thyroid Ultrasonic imaging Videos Solid modeling Feature extraction Image segmentation Cancer Deep learning thyroid nodules computer-aided diagnosis ultrasound videos detection and tracking

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Event Camera Based Real-Time detection and tracking of Indoor Ground Robots

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IEEE ACCESS 2021年 9卷 166588-166602页

作者： Iaboni, Craig Patel, Himanshu Lobo, Deepan Choi, Ji-Won Abichandani, Pramod New Jersey Inst Technol Ying Wu Coll Comp YWCC Newark NJ 07103 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark Coll Engn NCE Newark NJ 07103 USA New Jersey Inst Technol Newark Coll Engn NCE Robot & Data Lab RADLab Newark NJ 07103 USA

This paper presents a real-time method to detect and track multiple mobile ground robots using event cameras. The method uses density-based spatial clustering of applications with noise (DBSCAN) to detect the robots and a single k-dimensional (k - d) tree to accurately keep track of them as they move in an indoor arena. Robust detections and tracks are maintained in the face of event camera noise and lack of events (due to robots moving slowly or stopping). An off-the-shelf RGB camera-based tracking system was used to provide ground truth. Experiments including up to 4 robots are performed to study the effect of i) varying DBSCAN parameters, ii) the event accumulation time, iii) the number of robots in the arena, iv) the speed of the robots, v) variation in ambient light conditions on the detection and tracking performance, and vi) the effect of alternative clustering algorithms on detection performance. The experimental results showed 100% detection and tracking fidelity in the face of event camera noise and robots stopping for tests involving up to 3 robots (and upwards of 93% for 4 robots). When the lighting conditions were varied, a graceful degradation in detection and tracking fidelity was observed.

关键词： Event cameras multi-robot systems detection and tracking clustering and pattern recognition

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