3,397 research outputs found
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
Recently, mid-level features have shown promising performance in computer
vision. Mid-level features learned by incorporating class-level information are
potentially more discriminative than traditional low-level local features. In
this paper, an effective method is proposed to extract mid-level features from
Kinect skeletons for 3D human action recognition. Firstly, the orientations of
limbs connected by two skeleton joints are computed and each orientation is
encoded into one of the 27 states indicating the spatial relationship of the
joints. Secondly, limbs are combined into parts and the limb's states are
mapped into part states. Finally, frequent pattern mining is employed to mine
the most frequent and relevant (discriminative, representative and
non-redundant) states of parts in continuous several frames. These parts are
referred to as Frequent Local Parts or FLPs. The FLPs allow us to build
powerful bag-of-FLP-based action representation. This new representation yields
state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has
attracted increasing attention since the first work reported in 2010. Over this
period, many benchmark datasets have been created to facilitate the development
and evaluation of new algorithms. This raises the question of which dataset to
select and how to use it in providing a fair and objective comparative
evaluation against state-of-the-art methods. To address this issue, this paper
provides a comprehensive review of the most commonly used action recognition
related RGB-D video datasets, including 27 single-view datasets, 10 multi-view
datasets, and 7 multi-person datasets. The detailed information and analysis of
these datasets is a useful resource in guiding insightful selection of datasets
for future research. In addition, the issues with current algorithm evaluation
vis-\'{a}-vis limitations of the available datasets and evaluation protocols
are also highlighted; resulting in a number of recommendations for collection
of new datasets and use of evaluation protocols
Heterogeneous hand gesture recognition using 3D dynamic skeletal data
International audienceHand gestures are the most natural and intuitive non-verbal communication medium while interacting with a computer, and related research efforts have recently boosted interest. Additionally, the identifiable features of the hand pose provided by current commercial inexpensive depth cameras can be exploited in various gesture recognition based systems, especially for Human-Computer Interaction. In this paper, we focus our attention on 3D dynamic gesture recognition systems using the hand pose information. Specifically, we use the natural structure of the hand topology-called later hand skeletal data-to extract effective hand kinematic descriptors from the gesture sequence. Descriptors are then encoded in a statistical and temporal representation using respectively a Fisher kernel and a multi-level temporal pyramid. A linear SVM classifier can be applied directly on the feature vector computed over the whole presegmented gesture to perform the recognition. Furthermore, for early recognition from continuous stream, we introduced a prior gesture detection phase achieved using a binary classifier before the final gesture recognition. The proposed approach is evaluated on three hand gesture datasets containing respectively 10, 14 and 25 gestures with specific challenging tasks. Also, we conduct an experiment to assess the influence of depth-based hand pose estimation on our approach. Experimental results demonstrate the potential of the proposed solution in terms of hand gesture recognition and also for a low-latency gesture recognition. Comparative results with state-of-the-art methods are reported
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