DLAM 2019 : Deep Learning for Activity Monitoring

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in conjunction with the 16th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2019
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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.

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TOPICS
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
Scene understanding
Sensor calibration
Event detection
Real-time applications
Advancements in deep learning