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Erschienen in:
Real-Time Semantic Segmentation with Label Propagation Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Online Multi-target Tracking with Strong and Weak Detections

verfasst von : Ricardo Sanchez-Matilla, Fabio Poiesi, Andrea Cavallaro

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

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Abstract

We propose an online multi-target tracker that exploits both high- and low-confidence target detections in a Probability Hypothesis Density Particle Filter framework. High-confidence (strong) detections are used for label propagation and target initialization. Low-confidence (weak) detections only support the propagation of labels, i.e. tracking existing targets. Moreover, we perform data association just after the prediction stage thus avoiding the need for computationally expensive labeling procedures such as clustering. Finally, we perform sampling by considering the perspective distortion in the target observations. The tracker runs on average at 12 frames per second. Results show that our method outperforms alternative online trackers on the Multiple Object Tracking 2016 and 2015 benchmark datasets in terms tracking accuracy, false negatives and speed.

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Vorheriges Kapitel Multi-class Multi-object Tracking Using Changing Point Detection
Nächstes Kapitel Multi-person Tracking by Multicut and Deep Matching
Fußnoten
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Larger values of \(\tau _s\) reduce the number of false positives and lead to a more conservative initialization of the trajectories.
 
3
Last accessed on \(10^{th}\) August 2016.
 
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Metadaten
Titel
Online Multi-target Tracking with Strong and Weak Detections
verfasst von
Ricardo Sanchez-Matilla
Fabio Poiesi
Andrea Cavallaro
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-48880-6

Electronic ISBN: 978-3-319-48881-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48881-3_7

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