Section: New Results

Introduction

This year Stars has proposed new results related to its three main research axes : perception for activity recognition, semantic activity recognition and software engineering for activity recognition.

• Pedestrian Detection using Convolutional Neural Networks (see   7.2 )

• Head detection for eye tracking application (see 7.3 )

• Minimizing hallucination in Histogram of Oriented Gradients (see   7.4 )

• Hybrid approaches for Gender estimation (see 7.5 )

• Automated Healthcare: Facial-expression-analysis for Alzheimer's patients in musical mnemotherapy (see 7.6 )

• Robust Global Tracker based on an Online Estimation of Tracklet Descriptor Reliability (see 7.7 )

• Optimizing people tracking for a video-camera network (see   7.8 )

• Multi-camera Multi-object Tracking and Trajectory Fusion (see 7.9 )

• Person Re-Identification in Real-World Surveillance Systems (see 7.10 )

• Human Action Recognition in Videos (see 7.11 )

• Evaluation of Event Recognition without using Ground Truth (see 7.12 )

• Semantic Event Fusion of Different Visual Modality Concepts for Activity Recognition (see 7.13 )

• Semi-supervised activity recognition using high-order temporal-composite patterns of visual concepts (see 7.14 )

• From activity recognition to the assessment of seniors' autonomy (see 7.15 )

• Serious Games Interfaces using an RGB-D camera (see   7.16 )

• Assistance for Older Adults in Serious Game using an Interactive System (see 7.17 )

• Generating Unsupervised Models for Online Long-Term Daily Living Activity Recognition (see 7.18 )

• Run-time Adaptation of Video Systems (see 7.19 )

• Scenario Description Language (see 7.20 )

• Scenario Recognition (see 7.21 )

• The Clem Workflow (see 7.22 )

• Safe Composition in WComp Middleware for Internet of Things (see 7.23 )

• Design of UHD panoramic video camera (see 7.24 )

• Brick & Mortar Cookies (see 7.25 )

• Monitoring Older People Experiments (see 7.26 )