NDLI: Semi-supervised Learning of Feature Hierarchies for Object Detection in a Video

Content Provider	IEEE Xplore Digital Library
Author	Yang Yang Guang Shu Shah, M.
Copyright Year	2013
Description	Author affiliation: Center for Res. in Comput. Vision, Univ. of Central Florida, Orlando, FL, USA (Yang Yang; Guang Shu; Shah, M.)
Abstract	We propose a novel approach to boost the performance of generic object detectors on videos by learning video-specific features using a deep neural network. The insight behind our proposed approach is that an object appearing in different frames of a video clip should share similar features, which can be learned to build better detectors. Unlike many supervised detector adaptation or detection-by-tracking methods, our method does not require any extra annotations or utilize temporal correspondence. We start with the high-confidence detections from a generic detector, then iteratively learn new video-specific features and refine the detection scores. In order to learn discriminative and compact features, we propose a new feature learning method using a deep neural network based on auto en-coders. It differs from the existing unsupervised feature learning methods in two ways: first it optimizes both discriminative and generative properties of the features simultaneously, which gives our features better discriminative ability, second, our learned features are more compact, while the unsupervised feature learning methods usually learn a redundant set of over-complete features. Extensive experimental results on person and horse detection show that significant performance improvement can be achieved with our proposed method.
Starting Page	1650
Ending Page	1657
File Size	2197936
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.216
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Detectors Image color analysis Learning systems Training Neural networks Object detection video analysis feature learning deep learning
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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