Deep Learning for Activity Monitoring- DLAM 2019

September 21, Taipei, Taiwan

Scope & Topics

In last decade, Deep Learning has become the most used approach to any computer vision problem; on the other hand, there has been a growing diffusion of many different kinds of the sensing device (static and mobile) for environmental monitoring and surveillance purposes.
The focus of the Workshop is on the application of Deep Learning approaches to activity recognition, with special attention to real applications in real contexts.
We encourage researchers to formulate innovative feature representations, learning methodologies, and end-to-end vision systems based on deep learning. The aim of this workshop is to bring together researchers from different communities (such as Computer Vision, networked embedded sensing, artificial intelligence and so on) which address both the main topics of Deep Learning and Activity Recognition.

Topics of Interest :

• Single and multiple object tracking
• Re-identification
• Human behavior analysis
• Deep Learning in embedded systems
• Deep Learning for crowd analysis
• Individual activity detection and recognition
• Multi-agent/multi-sensing activity detection and recognition
• Sensor calibration
• Event detection
• Real-time applications
• Advancements in deep learning
  

Important Dates

Submission Deadline	:	July 06, 2019
Authors Notification	:	July 14, 2019
Final Manuscript Due	:	July 29, 2019

 

User Name : austin
Posted 11-04-2019 on 22:53:15 AEDT

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