Real-time Face Tracking Method using Improved CamShift

Lee, Jun-Hwan; Yoo, Ji-Sang

doi:10.5909/jbe.2016.21.6.861

Cited by 5 publications

(6 citation statements)

References 30 publications

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“…Table 1 shows the results of the error calculation of the parameters of state vectors in good lighting room and poor lighting room conditions. [30] is used as a similarity function to calculate the degree of similarity between two probability distributions. Tis is an efcient way to measure the similarity of two probability distributions.…”

Section: Kalman Filtermentioning

confidence: 99%

An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter

Nguyen

Chu

Phan

et al. 2023

Journal of Robotics

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Automated human tracking in real time has been applied in many areas such as security, surveillance, traffic control, and robots. In this paper, an improvement of the Camshift human tracking algorithm based on deep learning and the Kalman filter is proposed. To detail an approach by using YOLOv4-tiny to detect a human in real time, Camshift is used to track a particular person and the Kalman filter is applied to enhance the performance of this algorithm in case of occlusion, noise, and different light conditions. The experiments show that the combination of YOLOv4-tiny and the improved Camshift algorithm raises the standard of speed as well as robustness. The proposed algorithm is suitable for running in real time and adapts well to the same color and different light conditions.

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Section: Kalman Filtermentioning

confidence: 99%

An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter

Nguyen

Chu

Phan

et al. 2023

Journal of Robotics

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“…Cam-shift algorithm is the commonly used gesture tracking algorithm, which is good for tracking solid objects in a black-and-white background. However, the contrast between the background color and the target is not obvious, and the tracking effect is poor [15][16][17][38][39][40]. To verify the effectiveness of the switching federated filter algorithm, a gesture tracking and positioning experiment based on the 3D interactive software is presented.…”

Section: Gesture Tracking and Positioning Experiments Based On D Imentioning

confidence: 99%

“…The definition of human-computer interaction technology is based on a certain software program through the corresponding input and output devices, and the organic combination of computer and human operations is employed to realize the related technology of interactive communication [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. The current large-screen human-computer interaction technology is mainly divided into the following categories: (1) infrared frame touch interaction; (2) capacitive touch-based electronic screen touch interaction; (3) speech recognition interaction; (4) computer vision interaction.…”

Section: Introductionmentioning

confidence: 99%

Large‐Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

Hou

2018

Complexity

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Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated filtering algorithm work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction.

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“…For vehicle tracking, many researchers have conducted in-depth research and have proposed many target tracking algorithms, such as traditional Kalman filter algorithm [17], deep learning [18], Meanshift algorithm [19], clustering algorithm [20], Camshift algorithm [21], aggregation algorithm [22], and particle filter algorithm [23]. In recent years, as deep learning technology has shined in computer vision community, especially since 2015, GPUs have become widespread, and the deep learning algorithm has also been introduced into target tracking.…”

Section: Introductionmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Sun

Zhang

et al. 2020

Complexity

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Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.

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Real-time Face Tracking Method using Improved CamShift

Cited by 5 publications

References 30 publications

An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter

An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter

Large‐Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

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