Argus: Efficient Activity Detection System for Extended Video Analysis

Abstract

We propose an Efficient Activity Detection System, Argus, for Extended Video Analysis in the surveillance scenario. For the spatial-temporal event detection in the surveillance video, we first generate video proposals by applying object detection and tracking algorithm which shared the detection features. After that, we extract several different features and apply sequential activity classification with them. Finally, we eliminate inaccurate events and fuse all the predictions from different features.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2020
Accession Number
AD1154831

Entities

People

  • Alexander G. Hauptmann
  • Guoliang Kang
  • Jing Wen
  • Junwei Liang
  • Liangke Gui
  • Lijun Yu
  • Peng Chen
  • Po-yao Huang
  • Wenhe Liu
  • Xiaojun Chang
  • Yijun Qian

Organizations

  • Carnegie Mellon University
  • Monash University

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Vision
  • Computers
  • Detection
  • Event Detection
  • Feature Extraction
  • Identification
  • Image Processing
  • Image Recognition
  • Information Processing
  • Information Systems
  • Machine Learning
  • Neural Networks
  • Parallel Computing
  • Pattern Recognition
  • Recognition
  • Video Surveillance

Fields of Study

  • Computer science

Readers

  • Coastal Oceanography
  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Distributed Systems and Data Platform Development