Research of CamShift Algorithm to Track Motion Objects

Zhu, Li; Hu, Tao

doi:10.11591/telkomnika.v11i8.3037

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…This CS algorithm identifies and tracks the GMT based on Hue-Saturation-Value (HSV) color histogram new PDF of each frame [31], [32], [33], [34], [35], [36].…”

Section: B Cs Algorithmmentioning

confidence: 99%

A Multi-Level Visual Tracking Algorithm for Autonomous Vehicles

Kanth²,

Guntupalli³

et al. 2017

ijarcs

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A multi-level visual tracking algorithm is proposed for autonomous vehicles based on mean-shift algorithm, cam-shift algorithm and extended kalman filter estimator. The proposed multi-level visual tracking algorithm is implemented and included in simulation to check its performance. Simulation results are captured after applying existing algorithms, proposed multi-level visual tracking algorithm and observed their performance. The simulated results show that the proposed multi-level visual tracking algorithm identifies and tracks the ground moving target efficiently.

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“…This CS algorithm identifies and tracks the GMT based on Hue-Saturation-Value (HSV) color histogram new PDF of each frame [31], [32], [33], [34], [35], [36].…”

Section: B Cs Algorithmmentioning

confidence: 99%

A Multi-Level Visual Tracking Algorithm for Autonomous Vehicles

Kanth²,

Guntupalli³

et al. 2017

ijarcs

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Comparative Study of CAMSHIFT and RANSAC Methods for Face and Eye Tracking in Real-Time Video

Raghuveera

Vidhushini

Swathi

2017

International Journal of Intelligent Information Technologies

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Real-Time Facial and eye tracking is critical in applications like military surveillance, pervasive computing, Human Computer Interaction etc. In this work, face and eye tracking are implemented by using two well-known methods, CAMSHIFT and RANSAC. In our first approach, a frontal face detector is run on each frame of the video and the Viola-Jones face detector is used to detect the faces. CAMSHIFT Algorithm is used in the real- time tracking along with Haar-Like features that are used to localize and track eyes. In our second approach, the face is detected using Viola-Jones, whereas RANSAC is used to match the content of the subsequent frames. Adaptive Bilinear Filter is used to enhance quality of the input video. Then, we run the Viola-Jones face detector on each frame and apply both the algorithms. Finally, we use Kalman filter upon CAMSHIFT and RANSAC and compare with the preceding experiments. The comparisons are made for different real-time videos under heterogeneous environments through proposed performance measures, to identify the best-suited method for a given scenario.

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Research of CamShift Algorithm to Track Motion Objects

Cited by 2 publications

References 3 publications

A Multi-Level Visual Tracking Algorithm for Autonomous Vehicles

A Multi-Level Visual Tracking Algorithm for Autonomous Vehicles

Comparative Study of CAMSHIFT and RANSAC Methods for Face and Eye Tracking in Real-Time Video

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