Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Video anomaly detection with self-supervised deep nets

Arshak Navruzyan (platform.ai)
17:2518:05 Wednesday, 24 May 2017
Data science and advanced analytics
Location: Capital Suite 7
Level: Intermediate
Average rating: ****.
(4.80, 5 ratings)

Who is this presentation for?

  • Security professionals, chief security officers, and data scientists

Prerequisite knowledge

  • General knowledge of machine learning and information security

What you'll learn

  • Explore the current state of the art for video analysis using deep learning techniques and the associated challenges

Description

Factories could improve worker safety and reduce costs from machine, robot, and worker error through incisive use of state-of-the-art video deep learning techniques. Specifically, deep learning can be used to detect anomalies in video recordings of factory workers. Such anomalies may include faults in the machines, worker behavior, improper use of safety uniforms, and anomalies in robot performance.

Deep learning affords novel and powerful techniques for video prediction and analysis. Arshak Navruzyan explores the current state of the art for video analysis using deep learning techniques and the associated challenges.

Photo of Arshak Navruzyan

Arshak Navruzyan

platform.ai

Arshak is a machine learning focused product manager. He founded Fellowship.AI applied machine learning fellowship program and is a cofounder of Platform.AI.

He has delivered AI solutions for some of the largest enterprises in the world and multi-billion dollar quantitative hedge funds.

Previously Arshak served as the Chief Technology Officer at Sentient Technologies. He has also been in technology leadership roles at Argyle Data, Alpine, Endeca/Oracle.