Title :
Interactive object detection
Author :
Yao, Angela ; Gall, Juergen ; Leistner, Christian ; Van Gool, Luc
Author_Institution :
ETH Zurich, Zurich, Switzerland
Abstract :
In recent years, the rise of digital image and video data available has led to an increasing demand for image annotation. In this paper, we propose an interactive object annotation method that incrementally trains an object detector while the user provides annotations. In the design of the system, we have focused on minimizing human annotation time rather than pure algorithm learning performance. To this end, we optimize the detector based on a realistic annotation cost model based on a user study. Since our system gives live feedback to the user by detecting objects on the fly and predicts the potential annotation costs of unseen images, data can be efficiently annotated by a single user without excessive waiting time. In contrast to popular tracking-based methods for video annotation, our method is suitable for both still images and video. We have evaluated our interactive annotation approach on three datasets, ranging from surveillance, television, to cell microscopy.
Keywords :
object detection; video signal processing; video surveillance; annotation cost model; cell microscopy; digital image; digital video data; human annotation time minimisation; image annotation; interactive annotation approach; interactive object annotation method; interactive object detection; live feedback; surveillance; television; Detectors; Humans; Microscopy; Object detection; Predictive models; Training; Vegetation;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4673-1226-4
Electronic_ISBN :
1063-6919
DOI :
10.1109/CVPR.2012.6248060