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Author: Avi Bleiweiss

Affiliation: BShalem Research, United States

ISBN: 978-989-758-203-5

Keyword(s): Stream Data, Approximate Counting, Sliding Window, Cosine Distance, Surveillance Video, Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Context Discovery ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Multimedia Data ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:
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

@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},
}

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

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