Peeking into the Future: Predicting Future Person Activities and Locations in Videos

Abstract

Deciphering human behaviors to predict their future paths/trajectories and what they would do from videos is important in many applications. Motivated by this idea, this paper studies predicting a pedestrians future path jointly with future activities. We propose an end-to-end, multi-task learning system utilizing rich visual features about human behavioral information and interaction with their surroundings.

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Document Details

Document Type
Technical Report
Publication Date
Jun 16, 2019
Accession Number
AD1152117

Entities

People

  • Alexander Hauptmann
  • Fei-Fei Li
  • Juan Carlos Niebles
  • Junwei Liang
  • Lu Jiang

Organizations

  • Carnegie Mellon University
  • Stanford University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Computer Graphics
  • Computer Languages
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Data Displays
  • Detection
  • Dimensionality Reduction
  • Human Behavior
  • Information Science
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Pattern Recognition
  • Recognition
  • Recurrent Neural Networks

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Systems Analysis and Design