ClickBAIT-v2: Training an Object Detector in Real-Time

Ervin Teng; Rui Huang; Bob Iannucci

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arXiv · 1803.10358

ClickBAIT-v2: Training an Object Detector in Real-Time

Ervin Teng·Rui Huang·Bob Iannucci

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Abstract

Modern deep convolutional neural networks (CNNs) for image classification and object detection are often trained offline on large static datasets. Some applications, however, will require training in real-time on live video streams with a human-in-the-loop. We refer to this class of problem as time-ordered online training (ToOT). These problems will require a consideration of not only the quantity of incoming training data, but the human effort required to annotate and use it. We demonstrate and evaluate a system tailored to training an object detector on a live video stream with minimal input from a human operator. We show that we can obtain bounding box annotation from weakly-supervised single-point clicks through interactive segmentation. 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 3-4 times.

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BibTeX RIS

Ervin Teng, Rui Huang, Bob Iannucci. 2018-03-27. ClickBAIT-v2: Training an Object Detector in Real-Time. https://arxiv.org/abs/1803.10358

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