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Discovering Multi-relational Latent Attributes by Visual Similarity Networks Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Spatio-Temporal Consistency for Head Detection in High-Density Scenes

Authors : Emanuel Aldea, Davide Marastoni, Khurom H. Kiyani

Published in: Computer Vision - ACCV 2014 Workshops

Publisher: Springer International Publishing

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Abstract

In this paper we address the problem of detecting reliably a subset of pedestrian targets (heads) in a high-density crowd exhibiting extreme clutter and homogeneity, with the purpose of obtaining tracking initializations. We investigate the solution provided by discriminative learning where we require that the detections in the image space be localized over most of the target area and temporally stable. The results of our tests show that discriminative learning strategies provide valuable cues about the target localization which may be combined with other complementary strategies in order to bootstrap tracking algorithms in these challenging environments.

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previous chapter Cross Dataset Person Re-identification
next chapter Multi-target Tracking with Sparse Group Features and Position Using Discrete-Continuous Optimization
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Metadata
Title
Spatio-Temporal Consistency for Head Detection in High-Density Scenes
Authors
Emanuel Aldea
Davide Marastoni
Khurom H. Kiyani
Copyright Year
2015
Book
Computer Vision - ACCV 2014 Workshops

Print ISBN: 978-3-319-16633-9

Electronic ISBN: 978-3-319-16634-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-16634-6_48

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