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Section: New Software and Platforms
sup_ad_ont
SUP Activity detection with ontologies
Keyword: Activity recognition
Functional Description: SUP plugin for activity detection, with manually defined ontologies.
Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
- Introduction
- Pedestrian Detection: Training Set Optimization
- Detection of Pedestrians Using Deep Learning
- Deep Learning applied on Embedded Systems for people detection
- Facial Analysis
- Multi-Object Tracking using Multi-Channel Part Appearance Representation
- Tracklets Pre-Processing for Signature Computation in the Context of Multi-Shot Person Re-Identification
- Multi-shot Person Re-identification in surveillance videos
- Person Re-Identification using Pose-Driven Body Parts
- Human Action Recognition in Videos with Local Representation
- Action Detection in Untrimmed Videos
- RGB-D based Action Recognition using CNNs
- Recognizing Human Actions Using RGB Sport Videos From the Web
- Event Recognition Based on Depth Image
- Recognition of Daily Activities by Embedding Visual Features within a Semantic Language
- Cognitive Assessment Using Gesture Recognition
- Geometric and Visual Features Fusion for Action Recognition
- Probabilistic Logic for Activity Recognition
- Recognizing Retracing of Steps Using Walk Comparison
- Safe & Easy Environment for Alzheimer Disease and related disorders
- Early detection of cognitive disorders such as dementia on the basis of speech analysis ELEMENT
- Serious exergames for Cognitive Stimulation
- Activity Description Language
- The Clem Workflow
- Study of Temporal Properties of Neuronal Archetypes
- Maintaining the engagement of older adults with dementia while interacting with serious game
- Application of deep learning on healthcare
- Brick & Mortar Cookies
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography