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Erschienen in:
Integrating Domain Knowledge: Using Hierarchies to Improve Deep Classifiers Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Assessing the Impact of Video Compression on Background Subtraction

verfasst von : Poonam Beniwal, Pranav Mantini, Shishir K. Shah

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Video cameras are deployed in large numbers for surveillance. Video is compressed and transmitted over bandwidth limited communication channels. Researches have increasingly focused on automated analysis algorithms to understand video. This raises the question “Does compression have an effect on automated analysis algorithms?”. This is a vital question in understanding the reliability of automated analysis algorithms. In this paper, we evaluated background subtraction algorithm’s performance on videos acquired using a typical surveillance camera under different scenarios. The videos in the dataset are collected from an IP-based surveillance camera and compressed using different bitrates and quantization levels. The experimental results provide insights on the different compression settings and their impact on the expected performance of background subtraction algorithms. Furthermore, we train a classifier that utilizes video quality to predict the performance of an algorithm.

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Vorheriges Kapitel Using Motion Compensation and Matrix Completion Algorithm to Remove Rain Streaks and Snow for Video Sequences
Nächstes Kapitel Learning Motion Regularity for Temporal Video Segmentation and Anomaly Detection
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Metadaten
Titel
Assessing the Impact of Video Compression on Background Subtraction
verfasst von
Poonam Beniwal
Pranav Mantini
Shishir K. Shah
Copyright-Jahr
2020
Buch
Pattern Recognition

Print ISBN: 978-3-030-41403-0

Electronic ISBN: 978-3-030-41404-7

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-41404-7_8

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