An integrated robust approach for fast face tracking in noisy Real-World videos with visual constraints

Ranganatha, S; Gowramma, Y P

doi:10.1109/icacci.2017.8125935

Cited by 9 publications

(13 citation statements)

References 11 publications

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“…We have compared the current (proposed) algorithm with five other similar (and robust) algorithms developed in recent times. The five algorithms are KLT [11][12][13], CAMSHIFT [14], Algorithm* [1], Algorithm# [16] and Algorithm^ [17]. These algorithms were tested with all the videos that are considered for the computation; out of which, results of 15 videos have been tabulated for reference purpose.…”

Section: Results and Analysismentioning

confidence: 99%

“…But, the algorithm is capable of tracking only single face in video sequences captured using unmoving camera and moving face(s) category. Ranganatha S et al [17] have developed another algorithm by integrating CAMSHIFT [14] and Kalman filter [18][19][20]. Kalman filter minimizes noise and updates current frame information to the next frame in video.…”

Section: Related Workmentioning

confidence: 99%

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Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences

Ranganatha¹,

Gowramma²

2018

IJIGSP

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We are proposing a novel algorithm for tracking human face(s) in different background video sequences. We have trained both face and non-face images which help in face(s) detection process. At first, FAST features and corner points are extracted from the detected face(s). Further, mid points are calculated from corner points. FAST features, corner points and mid points are combined together. Using the combined points, point tracker tracks face(s) in the frames of the video sequence. Standard metrics were adopted for measuring the performance of the proposed algorithm. Low resolution video sequences with challenges such as partial occlusion, changes in expression, variations in illumination and pose took part while testing the proposed algorithm. Test results clearly indicate the robustness of the proposed algorithm on all different background challenging video sequences.

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Section: Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences

Ranganatha¹,

Gowramma²

2018

IJIGSP

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show abstract

“…This section talks about experimental results of the proposed algorithm. By means of the performance metrics discussed before, the outcomes of the proposed algorithm are then equated with CAMSHIFT and Algorithm* [19].…”

Section: Results and Analysismentioning

confidence: 99%

“…VIDEO 1 2 3 4 5 6 7 8 9 10 R 1 1 1 1 1 1 1 1 1 1 P 1 1 1 1 1 1 1 1 1 1 TNR 0 0 0 0 0 0 0 0 0 0 NPV 0 0 0 0 0 0 0 0 0 0 FNR 0 0 0 0 0 0 0 0 0 0 FPR 0 0 0 0 0 0 0 0 0 0 A 1 1 1 1 1 1 1 1 1 1 F1S 1 1 1 1 1 1 1 Table 3. Quantifiable probabilistic outcomes of the Algorithm* [19] algorithm for 10 video sequences. VIDEO 1 2 3 4 5 6 7 8 9 10 R -------1 --P -------1 --TNR -------0 --NPV -------0 --FNR -------0 --FPR -------0 --A -------1 --F1S -…”

Section: Results and Analysismentioning

confidence: 99%

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Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence

Ranganatha¹,

Gowramma²

2018

IJITCS

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Due to the lack of particular algorithms for automatic detection and tracking of person face(s), we have developed a new algorithm to achieve detection and single/multiple face tracking in different background video sequence. To detect faces, skin sections are segmented from the frame by means of YCbCr color model; and facial features are used to agree whether these sections contain person face or not. This procedure is challenging, because face color is unique and some objects may have similar color. Further, color and Eigen features are extracted from detected faces. Based on the points detected in facial region, point tracker tracks the user specified number of faces throughout the video sequence. The developed algorithm was tested on challenging dataset videos; and measured for performance using standard metrics. Test results obtained ensure the efficiency of proposed algorithm at the end.

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Binocular Vision-Based Human Ranging Algorithm Based on Human Faces Recognition

Meng

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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An integrated robust approach for fast face tracking in noisy Real-World videos with visual constraints

Cited by 9 publications

References 11 publications

Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences

Image Training, Corner and FAST Features based Algorithm for Face Tracking in Low Resolution Different Background Challenging Video Sequences

Color Based New Algorithm for Detection and Single/Multiple Person Face Tracking in Different Background Video Sequence

Binocular Vision-Based Human Ranging Algorithm Based on Human Faces Recognition

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