Title

Semi-Supervised Learning Of Feature Hierarchies For Object Detection In A Video

Keywords

deep learning; feature learning; Object detection; video analysis

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. © 2013 IEEE.

Publication Date

11-15-2013

Publication Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Number of Pages

1650-1657

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2013.216

Socpus ID

84887365369 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84887365369

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