Author:
Avi Bleiweiss
Affiliation:
BShalem Research, United States
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.