Finding Top-kappa Unexplained Activities in Video

Abstract

Most past work on identifying unexpected activities in video has focused on looking for specific patterns of anomalous activities. In this paper, we consider the situation where we have a known set A of activities (normal and abnormal) that we wish to monitor. However, in addition, we wish to identify abnormal activities that have not been previously considered or encountered, i.e. they are not in A. We formally define the probability that a video sequence is unexplained (totally or partially) w.r.t. A. We develop efficient algorithms to identify the top-k Totally and Partially Unexplained Activities in a video w.r.t. A. Our algorithms use neat mathematical properties of the definitions for efficiency. We describe experiments using two real-world datasets showing that our approach works well in practice in terms of both running time and accuracy.

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Document Details

Document Type
Technical Report
Publication Date
Mar 09, 2012
Accession Number
ADA587505

Entities

People

  • Antonio Picariello
  • Cristian Molinaro
  • Fabio Persia
  • Massimiliano Albanese
  • V. S. Subrahmanian

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Automata
  • Computer Science
  • Data Sets
  • Electronic Mail
  • Image Processing
  • Language
  • Linear Programming
  • Models
  • Observation
  • Precision
  • Probability
  • Probability Distributions
  • Surveillance
  • Universities
  • Video Surveillance

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Theoretical Analysis.