ICTACT Journal on Image and Video Processing

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Selected Single Face Tracking in Technically Challenging Different Background Video Sequences using Combined Features


Affiliations
1 Department of Computer Science and Engineering, Government Engineering College, Hassan, India
2 Department of Computer Science and Engineering, Kalpataru Institute of Technology, India
3 Department of Computer Science and Engineering, Rajeev Institute of Technology, India
     

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The commonly identified limitations of video face trackers are, the inability to track human face in different background video sequences with the conditions like occlusion, low quality, abrupt motions and failing to track single face when it contain multiple faces. In this paper, we propose a novel algorithm to track human face in different background video sequences with the conditions listed above. The proposed algorithm describes an improved KLT tracker. We collect Eigen, FAST as well as HOG features and combine them together. The combined features are given to the tracker to track the face. The algorithm being proposed is tested on challenging datasets videos and measured for performance using the standard metrics.

Keywords

Track Human Face, Different Background, Video Sequences, KLT, Combined Features.
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  • Selected Single Face Tracking in Technically Challenging Different Background Video Sequences using Combined Features

Abstract Views: 248  |  PDF Views: 1

Authors

S. Ranganatha
Department of Computer Science and Engineering, Government Engineering College, Hassan, India
Y. P. Gowramma
Department of Computer Science and Engineering, Kalpataru Institute of Technology, India
G. N. Karthik
Department of Computer Science and Engineering, Rajeev Institute of Technology, India
A. S. Sharan
Department of Computer Science and Engineering, Government Engineering College, Hassan, India

Abstract


The commonly identified limitations of video face trackers are, the inability to track human face in different background video sequences with the conditions like occlusion, low quality, abrupt motions and failing to track single face when it contain multiple faces. In this paper, we propose a novel algorithm to track human face in different background video sequences with the conditions listed above. The proposed algorithm describes an improved KLT tracker. We collect Eigen, FAST as well as HOG features and combine them together. The combined features are given to the tracker to track the face. The algorithm being proposed is tested on challenging datasets videos and measured for performance using the standard metrics.

Keywords


Track Human Face, Different Background, Video Sequences, KLT, Combined Features.

References