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DLAM 2019 : Deep Learning for Activity Monitoring | |||||||||||||||
Link: http://dlam2019.isasi.cnr.it/ | |||||||||||||||
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Call For Papers | |||||||||||||||
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in conjunction with the 16th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2019 -- 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 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 |
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