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GTC ON-DEMAND

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Abstract:
We'll explain how to use Deep Features for enabling state-of-the-art results in visual object tracking. Visual object tracking is a difficult task in three respects, since (1) it needs to be performed in real-time, (2) the only available information about the object is an image region in the first frame, and (3) the internal object models needs to be updated in each frame. The use of Deep Features gives significant improvements regarding accuracy and robustness of the object tracker, but straightforward frame-wise updates of the object model become prohibitively slow for real-time performance. By introducing a compact representation of Deep Features, a smart updating mechanism, and exploiting systematically GPU implementations for feature extraction and optimization, real-time performance is achievable without jeopardizing tracking quality.
We'll explain how to use Deep Features for enabling state-of-the-art results in visual object tracking. Visual object tracking is a difficult task in three respects, since (1) it needs to be performed in real-time, (2) the only available information about the object is an image region in the first frame, and (3) the internal object models needs to be updated in each frame. The use of Deep Features gives significant improvements regarding accuracy and robustness of the object tracker, but straightforward frame-wise updates of the object model become prohibitively slow for real-time performance. By introducing a compact representation of Deep Features, a smart updating mechanism, and exploiting systematically GPU implementations for feature extraction and optimization, real-time performance is achievable without jeopardizing tracking quality.  Back
 
Topics:
Computer Vision, Intelligent Video Analytics, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7436
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