TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Visual Object Tracking Fusing CNN and Color Histogram based Tracker and Depth Estimation for Automatic Immersive Audio Mixing |
| 영문제목(English Title) |
Visual Object Tracking Fusing CNN and Color Histogram based Tracker and Depth Estimation for Automatic Immersive Audio Mixing |
| 저자(Author) |
Sung-Jun Park
Md. Mahbubul Islam
Joong-Hwan Baek
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| 원문수록처(Citation) |
VOL 14 NO. 03 PP. 1121 ~ 1141 (2020. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.
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| 키워드(Keyword) |
Immersive Audio
GOTURN
Mean-Shift
CNN
Color Histogram
Depth Estimation
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| 파일첨부 |
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