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- Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
- Using Rhythm Awareness in Long-Term Activity Recognition Kristof Van Laerhoven, David Kilian, and Bernt Schiele
- Sliding-Windows for Rapid Object Class Localization: a Parallel Technique
- Automatic Discovery of Meaningful Object Parts with Latent CRFs Paul Schnitzspan1
- Discriminative Structure Learning of Hierarchical Representations for Object Detection
- Dead Reckoning from the Pocket We present a novel approach to enable dead reckoning
- Multi-Cue Onboard Pedestrian Detection Christian Wojek Stefan Walk Bernt Schiele
- Unsupervised Discovery of Structure in Activity Data using Multiple Eigenspaces
- A Performance Evaluation of Single and Multi-Feature People Detection
- Joint sOc-EUSAI conference Grenoble, october 2005 Analyzing Features for Activity Recognition
- Sensing Location in the Pocket Computer Science Department
- Towards Less Supervision in Activity Recognition from Wearable Sensors T^am Hu`ynh and Bernt Schiele
- An Exploration of Daily Routine Modeling based on Bluetooth and GSM-data Ulrich Steinhoff, Bernt Schiele
- How Computer Vision can help in Outdoor Positioning
- People-Tracking-by-Detection and People-Detection-by-Tracking Mykhaylo Andriluka Stefan Roth Bernt Schiele
- Pictorial Structures Revisited: People Detection and Articulated Pose Estimation Mykhaylo Andriluka, Stefan Roth, and Bernt Schiele
- Extracting Structures in Image Collections for Object Recognition
- M. STARK., M. GOESELE, B. SCHIELE: LEARNING SHAPE MODELS FROM 3D CAD DATA 1 Back to the Future: Learning Shape Models
- Vision Based Victim Detection from Unmanned Aerial Vehicles Mykhaylo Andriluka1, Paul Schnitzspan1, Johannes Meyer2, Stefan Kohlbrecher1,
- Standing on the Shoulders of Other Researchers A Position Statement
- Dead Reckoning from the Pocket An Experimental Study Ulrich Steinhoff and Bernt Schiele
- All for one or one for all? Combining Heterogeneous Features for Activity Spotting Ulf Blanke, Bernt Schiele
- Multi-Graph Based Semi-Supervised Learning for Activity Recognition Maja Stikic
- An Analysis of Sensor-Oriented vs. Model-Based Activity Recognition Andreas Zinnen1,2
- Daily Routine Recognition through Activity Spotting
- Exploring Semi-Supervised and Active Learning for Activity Recognition Maja Stikic
- ADL Recognition Based on the Combination of RFID and Accelerometer Sensing
- Functional Object Class Detection Based on Learned Affordance Cues
- Sustained Logging and Discrimination of Sleep Postures with Low-Level, Wrist-Worn Sensors
- Probabilistic Combination of Visual Context Based Attention and Object Detection
- F2008-08-062 MULTI-LEVEL SENSORFUSION AND COMPUTER-VISION
- Discovery of Activity Patterns using Topic Models T^am Hu`ynh, Mario Fritz and Bernt Schiele
- Scalable Recognition of Daily Activities with Wearable Sensors
- Toward Recognition of Short and Non-Repetitive Activities from Wearable Sensors
- 0 10 20 30 40 50 60 Printer Beverage Office to
- Towards Human Motion Capturing using Gyroscopeless Orientation Estimation Computer Science, TU Darmstadt
- May 17, 2010 | Ulf Blanke | Standing on the Shoulders of other Researchers... | 1 Standing on the Shoulder's of other Researchers
- Towards a Recognition of Short and Non Repetitive Activities from Wearable Sensors
- Demo Abstract: Whac-A-Bee A Sensor Network Game Eugen Berlin Pablo Guerrero Arthur Herzog , Daniel Jacobi ,
- Multi Activity Recognition based on Bodymodel-Derived Primitives
