Real-time dynamic gesture recognition and hand servo tracking using PTZ camera

Cao, Songxiao; Wang, Xuanyin

doi:10.1007/s11042-019-07869-7

Cited by 6 publications

(6 citation statements)

References 28 publications

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“…The following is a detailed description of various methods. • Fingertip detection based on YCbCr skin color segmentation [31].…”

Section: ) Test In Complex Backgroundsmentioning

confidence: 99%

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Fingertip Detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

Feng

et al. 2023

IEEE Access

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“…The following is a detailed description of various methods. • Fingertip detection based on YCbCr skin color segmentation [31].…”

Section: ) Test In Complex Backgroundsmentioning

confidence: 99%

“…During the training experiment, the fingertip positions in the 1000 singlefinger hand images mentioned in section VI-A were first marked, and then the YOLOv5 model was trained. • Fingertip based on YOLO-YCbCr [11], [31].…”

Section: ) Test In Complex Backgroundsmentioning

confidence: 99%

Fingertip Detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

Feng

et al. 2023

IEEE Access

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“…Figure 5 shows the definition of FBE. NCC compares the surrounding patch of the tracked point from the last frame (P1) to the current frame (P2), the definition of which is defined in Formula (1).…”

Section: Tld Working Principlementioning

confidence: 99%

“…Visual object tracking, as an important research field of computer vision, is a fundamental part of many computer vision systems, such as military navigation, human-computer interaction, unmanned aerial vehicle video analysis, dynamic behavioral recognition, intelligent medical treatment, and so on (see Figure 1) [1][2][3]. Given the location and extent of an arbitrary object, the task of visual object tracking is to determine the object's location with the best possible accuracy in the following frames [4].…”

Section: Introductionmentioning

confidence: 99%

A Visual Object Tracking Algorithm Based on Improved TLD

et al. 2020

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Visual object tracking is an important research topic in the field of computer vision. Tracking–learning–detection (TLD) decomposes the tracking problem into three modules—tracking, learning, and detection—which provides effective ideas for solving the tracking problem. In order to improve the tracking performance of the TLD tracker, three improvements are proposed in this paper. The built-in tracking module is replaced with a kernelized correlation filter (KCF) algorithm based on the histogram of oriented gradient (HOG) descriptor in the tracking module. Failure detection is added for the response of KCF to identify whether KCF loses the target. A more specific detection area of the detection module is obtained through the estimated location provided by the tracking module. With the above operations, the scanning area of object detection is reduced, and a full frame search is required in the detection module if objects fails to be tracked in the tracking module. Comparative experiments were conducted on the object tracking benchmark (OTB) and the results showed that the tracking speed and accuracy was improved. Further, the TLD tracker performed better in different challenging scenarios with the proposed method, such as motion blur, occlusion, and environmental changes. Moreover, the improved TLD achieved outstanding tracking performance compared with common tracking algorithms.

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“…Visual Object Tracking (VOT) is an active research topic in computer vision and machine learning due to extensive applications in areas including gesture recognition [1], sports analysis [2], visual surveillance [3], medical diagnosis [4], autonomous vehicles [5,6] and radar navigation systems [7][8][9]. Various factors such as partial or full occlusion, background clutter, illumination variation, deformation and other factors in the environment complicate a tracking problem [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

et al. 2020

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Object tracking is still an intriguing task as the target undergoes significant appearance changes due to illumination, fast motion, occlusion and shape deformation. Background clutter and numerous other environmental factors are other major constraints which remain a riveting challenge to develop a robust and effective tracking algorithm. In the present study, an adaptive Spatio-temporal context (STC)-based algorithm for online tracking is proposed by combining the context-aware formulation, Kalman filter, and adaptive model learning rate. For the enhancement of seminal STC-based tracking performance, different contributions were made in the proposed study. Firstly, a context-aware formulation was incorporated in the STC framework to make it computationally less expensive while achieving better performance. Afterwards, accurate tracking was made by employing the Kalman filter when the target undergoes occlusion. Finally, an adaptive update scheme was incorporated in the model to make it more robust by coping with the changes of the environment. The state of an object in the tracking process depends on the maximum value of the response map between consecutive frames. Then, Kalman filter prediction can be updated as an object position in the next frame. The average difference between consecutive frames is used to update the target model adaptively. Experimental results on image sequences taken from Template Color (TC)-128, OTB2013, and OTB2015 datasets indicate that the proposed algorithm performs better than various algorithms, both qualitatively and quantitatively.

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Real-time dynamic gesture recognition and hand servo tracking using PTZ camera

Cited by 6 publications

References 28 publications

Fingertip Detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

Fingertip Detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

A Visual Object Tracking Algorithm Based on Improved TLD

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

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