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Abstract:
In contrast to traditional CNN training with large offline static datasets, some autonomous machine applications will benefit from training in real-time for mid-mission adjustment and correction. This training will occur on live video streams, with a human-in-the-loop. We demonstrate and evaluate a system tailored to performing time-ordered online training (ToOT) in the field, capable of training an object detector on a live video stream with minimal input from a human operator. Online training is conducted entirely on an NVIDIA Jetson TX2 onboard an autonomous machine. We first define training benefit as a metric to measure the effectiveness of a user interaction in a ToOT sequence. We then show that we can obtain annotations for training an object detector from single-point clicks. Furthermore, by exploiting the time-ordered nature of the video stream through object tracking, we can increase the average training benefit of human interactions by several times.
In contrast to traditional CNN training with large offline static datasets, some autonomous machine applications will benefit from training in real-time for mid-mission adjustment and correction. This training will occur on live video streams, with a human-in-the-loop. We demonstrate and evaluate a system tailored to performing time-ordered online training (ToOT) in the field, capable of training an object detector on a live video stream with minimal input from a human operator. Online training is conducted entirely on an NVIDIA Jetson TX2 onboard an autonomous machine. We first define training benefit as a metric to measure the effectiveness of a user interaction in a ToOT sequence. We then show that we can obtain annotations for training an object detector from single-point clicks. Furthermore, by exploiting the time-ordered nature of the video stream through object tracking, we can increase the average training benefit of human interactions by several times.  Back
 
Topics:
Intelligent Video Analytics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8852
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