Efficient Distraction Detection in Surveillance Video using Approximate Counting over Decomposed Micro-streams

Avi Bleiweiss

2016

Abstract

Mining techniques of infinite data streams often store synoptic information about the most recently observed data elements. Motivated by space efficient solutions, our work exploits approximate counting over a fixed-size sliding window to detect distraction events in video. We propose a model that transforms inline the incoming video sequence to an orthogonal set of thousands of binary micro-streams, and for each of the bit streams we estimate at every timestamp the count of number-of-ones in a preceding sub-window interval. On window bound frames, we further extract a compact feature representation of a bag of count-of-1’s occurrences to facilitate effective query of transitive similarity samples. Despite its simplicity, our prototype demonstrates robust knowledge discovery to support the intuition of a context-neutral window summary. To evaluate our system, we use real-world scenarios from a video surveillance online-repository.

Paper Citation

in Harvard Style

Bleiweiss A. (2016). Efficient Distraction Detection in Surveillance Video using Approximate Counting over Decomposed Micro-streams . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 327-335. DOI: 10.5220/0006067103270335

in Bibtex Style

@conference{kdir16,
author={Avi Bleiweiss},
title={Efficient Distraction Detection in Surveillance Video using Approximate Counting over Decomposed Micro-streams},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={327-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006067103270335},
isbn={978-989-758-203-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Efficient Distraction Detection in Surveillance Video using Approximate Counting over Decomposed Micro-streams
SN - 978-989-758-203-5
AU - Bleiweiss A.
PY - 2016
SP - 327
EP - 335
DO - 10.5220/0006067103270335