116 research outputs found
Improving the Accuracy of Action Classification Using View-Dependent Context Information
Proceedings of: 6th International Conference, HAIS 2011, Wroclaw,
Poland, May 23-25, 2011This paper presents a human action recognition system that decomposes the task in two subtasks. First, a view-independent classifier, shared between the multiple views to analyze, is applied to obtain an initial guess of the posterior distribution of the performed action. Then, this posterior distribution is combined with view based knowledge to improve the action classification. This allows to reuse the view-independent component when a new view has to be analyzed, needing to only specify the view dependent knowledge. An example of the application of the system into an smart home domain is discussed.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/
TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/
TIC-1485) and DPS2008-07029-C02-02.Publicad
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
We present a comparative evaluation of various techniques for action
recognition while keeping as many variables as possible controlled. We employ
two categories of Riemannian manifolds: symmetric positive definite matrices
and linear subspaces. For both categories we use their corresponding nearest
neighbour classifiers, kernels, and recent kernelised sparse representations.
We compare against traditional action recognition techniques based on Gaussian
mixture models and Fisher vectors (FVs). We evaluate these action recognition
techniques under ideal conditions, as well as their sensitivity in more
challenging conditions (variations in scale and translation). Despite recent
advancements for handling manifolds, manifold based techniques obtain the
lowest performance and their kernel representations are more unstable in the
presence of challenging conditions. The FV approach obtains the highest
accuracy under ideal conditions. Moreover, FV best deals with moderate scale
and translation changes
Multicamera Action Recognition with Canonical Correlation Analysis and Discriminative Sequence Classification
Proceedings of: 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spain, May 30 - June 3, 2011.This paper presents a feature fusion approach to the recognition of human actions from multiple cameras that avoids the computation of the 3D visual hull. Action descriptors are extracted for each one of the camera views available and projected into a common subspace that maximizes the correlation between each one of the components of the projections. That common subspace is learned using Probabilistic Canonical Correlation Analysis. The action classification is made in that subspace using a discriminative classifier. Results of the proposed method are shown for the classification of the IXMAS dataset.Publicad
International Survey of Patients With IBS: Symptom Features and Their Severity, Health Status, Treatments, and Risk Taking to Achieve Clinical Benefit
While clinicians generally make treatment decisions in IBS related to the type of symptoms, other factors such as the perceived severity and the risks patients are willing to tolerate for effective treatment are also important to consider. These factors are not fully understood
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