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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

A Discriminative Framework for Anomaly Detection in Large Videos

verfasst von : Allison Del Giorno, J. Andrew Bagnell, Martial Hebert

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

We address an anomaly detection setting in which training sequences are unavailable and anomalies are scored independently of temporal ordering. Current algorithms in anomaly detection are based on the classical density estimation approach of learning high-dimensional models and finding low-probability events. These algorithms are sensitive to the order in which anomalies appear and require either training data or early context assumptions that do not hold for longer, more complex videos. By defining anomalies as examples that can be distinguished from other examples in the same video, our definition inspires a shift in approaches from classical density estimation to simple discriminative learning. Our contributions include a novel framework for anomaly detection that is (1) independent of temporal ordering of anomalies, and (2) unsupervised, requiring no separate training sequences. We show that our algorithm can achieve state-of-the-art results even when we adjust the setting by removing training sequences from standard datasets.

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Vorheriges Kapitel Ultra-Resolving Face Images by Discriminative Generative Networks
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Fußnoten
1
For simplicity, we describe the algorithm when the sliding window size and stride equal each other, \(t_w = \varDelta t_w\). It is just as valid to shorten the stride to increase accuracy. See Algorithm 1.
 
2
Ignoring the rare case when it occurs in the very first negative window.
 
3
Kakade provides a quick summary for those unfamiliar with these bounds [16]: http://​stat.​wharton.​upenn.​edu/​~skakade/​courses/​stat928/​lectures/​lecture06.​pdf.
 
4
The insights that follow generalize to VC dimension and other complexity measures.
 
5
For a useful introduction, see [19].
 
7
See supplementary material for more results, including results on individual videos in the UMN and Avenue datasets.
 
8
Other videos are unusable for a similar reason; for instance, 2 of the 8 videos available from [23] contain more than 50 % frames with at least one anomaly, and only 2 contain fewer than 20 % anomalous frames (one of which is the Subway exit sequence).
 
10
Consider the case when only 1 % of the video is anomalous: the EER on an algorithm that markes all frames normal would be 1 %, outperforming most modern algorithms. This extreme class imbalance is less prevalent in current standard datasets, but will become an apparent problem as more realistic datasets become prevalent.
 
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Metadaten
Titel
A Discriminative Framework for Anomaly Detection in Large Videos
verfasst von
Allison Del Giorno
J. Andrew Bagnell
Martial Hebert
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46453-4

Electronic ISBN: 978-3-319-46454-1

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46454-1_21