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Making Action Recognition Robust to Occlusions and Viewpoint Changes

Related publications (37)

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Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Hamidreza Bayati

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors can affect the data distribution between two different experimental sessions including sensor displacemen ...

Ieee Computer Soc Press, Customer Service Center, Po Box 3014, 10662 Los Vaqueros Circle, Los Alamitos, Ca 90720-1264 Usa2011

Learning Pose Invariant and Covariant Classifiers from Image Sequences

Mustafa Özuysal

Object tracking and detection over a wide range of viewpoints is a long-standing problem in Computer Vision. Despite significant advance in wide-baseline sparse interest point matching and development of robust dense feature models, it remains a largely op ...

EPFL2010

Fast Bounding Box Estimation based Face Detection

Sébastien Marcel, Venkatesh Bala Subburaman

The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as cascade), the scan ...

Idiap2010

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Context and activity recognition in complex scenarios is prone to data loss due to disconnections, sensor failure, transmission problems, etc. This generally implies significant changes in the recognition performance. In the case of classifier fusion fault ...

2010

A Multi-class Classification Strategy for Fisher Scores: Application to Signer Independent Sign Language Recognition

Oya Aran

Fisher kernels combine the powers of discriminative and generative classifiers by mapping the variable-length sequences to a new fixed length feature space, called the Fisher score space. The mapping is based on a single generative model and the classifier ...

2010

Class-specific classifiers for audio, visual and audio-visual speech recognition systems are developed and compared with traditional classifiers. We use state- of-the-art feature extraction methods and show the benefits of a class-specific classifier on ea ...

2010

OPPORTUNITY: Towards opportunistic activity and context recognition systems

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Daniel Roggen, Yu Fang

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific ...

2009

Probabilistic models for multi-classifier biometric authentication using quality measures

Jonas Richiardi

Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the p ...

EPFL2008

Classification with class-independent quality information for biometric verification

Krzysztof Kryszczuk

Biometric identity verification systems frequently face the challenges of non-controlled conditions of data acquisition. Under such conditions biometric signals may suffer from quality degradation due to extraneous, identity-independent factors. It has bee ...

EPFL2008

Keypoint Signatures for Fast Learning and Recognition

Pascal Fua, Vincent Lepetit, Michael Calonder

Statistical learning techniques have been used to dramatically speed-up keypoint matching by training a classifier to recognize a specific set of keypoints. However, the training itself is usually relatively slow and performed offline. Although methods hav ...

2008