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Published in:
Visual Features—From Early Concepts to Modern Computer Vision Read chapter Read first chapter

2013 | OriginalPaper | Chapter

13. Learning Object Detectors in Stationary Environments

Authors : Peter M. Roth, Sabine Sternig, Horst Bischof

Published in: Advanced Topics in Computer Vision

Publisher: Springer London

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Abstract

The most successful approach for object detection is still applying a sliding window technique, where a pre-trained classifier is evaluated on different locations and scales. In this chapter, we interrogate this strategy in the context of stationary environments. In particular, having a fixed camera position observing the same scene a lot of prior (spatio-temporal) information is available. Exploiting this specific scene information allows for (a) improving the detection performance and (b) for reducing the model complexity; both on reduced computational costs! These benefits are demonstrated for two different real-world tasks (i.e., person and car detection). In particular, we apply two different evaluation/update strategies (holistic, grid-based), where any suited online learner can be applied. In our case we demonstrate the proposed approaches for different applications and scenarios, clearly showing their benefits compared to generic methods.

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previous chapter Boosting k-Nearest Neighbors Classification
next chapter Video Temporal Super-resolution Based on Self-similarity
Footnotes
1
We refer a classifier to as an oracle, if it has a high precision, even at a low recall, and can thus be used to generate new training samples.
 
4
This particular task was chosen as implementations of existing approaches as well as a number of benchmark datasets are publicly available.
 
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Metadata
Title
Learning Object Detectors in Stationary Environments
Authors
Peter M. Roth
Sabine Sternig
Horst Bischof
Copyright Year
2013
Publisher
Springer London
Book
Advanced Topics in Computer Vision

Print ISBN: 978-1-4471-5519-5

Electronic ISBN: 978-1-4471-5520-1

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4471-5520-1_13

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