A New Contour-Based Approach to Moving Object Detection and Tracking Using a Low-End Three-Dimensional Laser Scanner

An, Jhonghyun; Choi, Baehoon; Kim, Hyun-Ju; Kim, Euntai

doi:10.1109/tvt.2019.2924268

Cited by 16 publications

(8 citation statements)

References 40 publications

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“…The motion energy model cannot describe the local information of the moving image. Therefore, it is necessary to use the local binary mode LBP to extract the local features of the moving image [21,22]. LBP operator can only cover part of the sphere moving image, but not all the frequency textures.…”

Section: Feature Extraction Based On Cnnmentioning

confidence: 99%

Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

Yuan

Cao

et al. 2022

Wireless Communications and Mobile Computing

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To improve the extraction accuracy of knee bending motion in ball motion image and reduce the extraction distance error and time consumption, a knee bending motion extraction algorithm using visual sensor is proposed. The visual sensor model is constructed based on the ball motion frame image, the trigger data is output through differential and logical judgment, and these data are normalized to generate the visual sensor sample set of the ball motion frame image. The sample set is used as the input of the convolution neural network (CNN) and the sample basis of the motion energy model. The CNN extracts the features of the sample set in the convolution layer, the motion energy model is combined with the local binary pattern to extract the features of the sample set, the weighted summation method is used to fuse the two features, and the Softmax classifier is used to classify and extract the knee bending motion. The results show that the proposed algorithm has good ball motion image collection effect, the knee bending motion extraction accuracy is always maintained at about 98%, the distance error is low, and the time consumption of ball motion feature extraction is only 2.65 s, which has high application value in the field of sports.

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Section: Feature Extraction Based On Cnnmentioning

confidence: 99%

Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

Yuan

Cao

et al. 2022

Wireless Communications and Mobile Computing

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“…is method is featured with simple and fast operation, can obtain a more comprehensive target image, and is less affected by light, but it is not applicable in the case of large background jitter. e so-called moving target tracking is to track the moving target in the video image sequence through the use of related algorithms and then analyze and evaluate the movement and behavior of the moving target relying on the obtained position and speed parameter information [15,16]. e current moving target tracking methods mainly include feature-based, area-based, and detection-based tracking methods.…”

Section: Moving Target Detection and Trackingmentioning

confidence: 99%

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

et al. 2021

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This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.

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“…Bird-Eye - [209][210][211][212][213][214][215][216][217][218][219] Directly provide the location and size information of the object…”

Section: Projection Methodsmentioning

confidence: 99%

“…ROIs were generated based on bird’s-eye view established from 2D Lidar with depth information, then the similarity measurement with loss function evaluation of vehicle template is established for vehicle detection. In [ 219 ], point clouds were directly projected to the bird’s-eye view, vehicles were detected based on the edge and contour features, and the detection areas containing vehicles were trimmed according to vehicle size information to achieve optimization.…”

Section: Vehicle Detection: Lidar-based Vehicle Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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A New Contour-Based Approach to Moving Object Detection and Tracking Using a Low-End Three-Dimensional Laser Scanner

Cited by 16 publications

References 40 publications

Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

Knee Bending Motion Extraction Algorithm for Ball Motion Image Using Vision Sensor

[Retracted] Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

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