2,481 research outputs found
Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation
Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions
ModDrop: adaptive multi-modal gesture recognition
We present a method for gesture detection and localisation based on
multi-scale and multi-modal deep learning. Each visual modality captures
spatial information at a particular spatial scale (such as motion of the upper
body or a hand), and the whole system operates at three temporal scales. Key to
our technique is a training strategy which exploits: i) careful initialization
of individual modalities; and ii) gradual fusion involving random dropping of
separate channels (dubbed ModDrop) for learning cross-modality correlations
while preserving uniqueness of each modality-specific representation. We
present experiments on the ChaLearn 2014 Looking at People Challenge gesture
recognition track, in which we placed first out of 17 teams. Fusing multiple
modalities at several spatial and temporal scales leads to a significant
increase in recognition rates, allowing the model to compensate for errors of
the individual classifiers as well as noise in the separate channels.
Futhermore, the proposed ModDrop training technique ensures robustness of the
classifier to missing signals in one or several channels to produce meaningful
predictions from any number of available modalities. In addition, we
demonstrate the applicability of the proposed fusion scheme to modalities of
arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure
Embodied learning of a generative neural model for biological motion perception and inference
Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons
Multi-Modality Human Action Recognition
Human action recognition is very useful in many applications in various areas, e.g. video surveillance, HCI (Human computer interaction), video retrieval, gaming and security. Recently, human action recognition becomes an active research topic in computer vision and pattern recognition. A number of action recognition approaches have been proposed. However, most of the approaches are designed on the RGB images sequences, where the action data was collected by RGB/intensity camera. Thus the recognition performance is usually related to various occlusion, background, and lighting conditions of the image sequences. If more information can be provided along with the image sequences, more data sources other than the RGB video can be utilized, human actions could be better represented and recognized by the designed computer vision system.;In this dissertation, the multi-modality human action recognition is studied. On one hand, we introduce the study of multi-spectral action recognition, which involves the information from different spectrum beyond visible, e.g. infrared and near infrared. Action recognition in individual spectra is explored and new methods are proposed. Then the cross-spectral action recognition is also investigated and novel approaches are proposed in our work. On the other hand, since the depth imaging technology has made a significant progress recently, where depth information can be captured simultaneously with the RGB videos. The depth-based human action recognition is also investigated. I first propose a method combining different type of depth data to recognize human actions. Then a thorough evaluation is conducted on spatiotemporal interest point (STIP) based features for depth-based action recognition. Finally, I advocate the study of fusing different features for depth-based action analysis. Moreover, human depression recognition is studied by combining facial appearance model as well as facial dynamic model
Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data
Sparse sensor placement is a central challenge in the efficient
characterization of complex systems when the cost of acquiring and processing
data is high. Leading sparse sensing methods typically exploit either spatial
or temporal correlations, but rarely both. This work introduces a new sparse
sensor optimization that is designed to leverage the rich spatiotemporal
coherence exhibited by many systems. Our approach is inspired by the remarkable
performance of flying insects, which use a few embedded strain-sensitive
neurons to achieve rapid and robust flight control despite large gust
disturbances. Specifically, we draw on nature to identify targeted
neural-inspired sensors on a flapping wing to detect body rotation. This task
is particularly challenging as the rotational twisting mode is three
orders-of-magnitude smaller than the flapping modes. We show that nonlinear
filtering in time, built to mimic strain-sensitive neurons, is essential to
detect rotation, whereas instantaneous measurements fail. Optimized sparse
sensor placement results in efficient classification with approximately ten
sensors, achieving the same accuracy and noise robustness as full measurements
consisting of hundreds of sensors. Sparse sensing with neural inspired encoding
establishes a new paradigm in hyper-efficient, embodied sensing of
spatiotemporal data and sheds light on principles of biological sensing for
agile flight control.Comment: 21 pages, 19 figure
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