318,965 research outputs found
Learning human actions by combining global dynamics and local appearance
In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-Euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods
Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition
Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been proposed that ex-tract representative features from 3D depth data, a recon-structed 3D surface mesh or more commonly from the re-covered estimate of the human skeleton. Recent advances in neuroscience have discovered a neural encoding of static 3D shapes in primate infero-temporal cortex that can be represented as a hierarchy of medial axis and surface fea-tures. We hypothesize a similar neural encoding might also exist for 3D shapes in motion and propose a hierarchy of dynamic medial axis structures at several spatio-temporal scales that can be modeled using a set of Linear Dynami-cal Systems (LDSs). We then propose novel discriminative metrics for comparing these sets of LDSs for the task of hu-man activity recognition. Combined with simple classifica-tion frameworks, our proposed features and corresponding hierarchical dynamical models provide the highest human activity recognition rates as compared to state-of-the-art methods on several skeletal datasets. 1
Substructure and Boundary Modeling for Continuous Action Recognition
This paper introduces a probabilistic graphical model for continuous action
recognition with two novel components: substructure transition model and
discriminative boundary model. The first component encodes the sparse and
global temporal transition prior between action primitives in state-space model
to handle the large spatial-temporal variations within an action class. The
second component enforces the action duration constraint in a discriminative
way to locate the transition boundaries between actions more accurately. The
two components are integrated into a unified graphical structure to enable
effective training and inference. Our comprehensive experimental results on
both public and in-house datasets show that, with the capability to incorporate
additional information that had not been explicitly or efficiently modeled by
previous methods, our proposed algorithm achieved significantly improved
performance for continuous action recognition.Comment: Detailed version of the CVPR 2012 paper. 15 pages, 6 figure
Towards Active Event Recognition
Directing robot attention to recognise activities and to anticipate events like goal-directed actions is a crucial skill for human-robot interaction. Unfortunately, issues like intrinsic time constraints, the spatially distributed nature of the entailed information sources, and the existence of a multitude of unobservable states affecting the system, like latent intentions, have long rendered achievement of such skills a rather elusive goal. The problem tests the limits of current attention control systems. It requires an integrated solution for tracking, exploration and recognition, which traditionally have been seen as separate problems in active vision.We propose a probabilistic generative framework based on a mixture of Kalman filters and information gain maximisation that uses predictions in both recognition and attention-control. This framework can efficiently use the observations of one element in a dynamic environment to provide information on other elements, and consequently enables guided exploration.Interestingly, the sensors-control policy, directly derived from first principles, represents the intuitive trade-off between finding the most discriminative clues and maintaining overall awareness.Experiments on a simulated humanoid robot observing a human executing goal-oriented actions demonstrated improvement on recognition time and precision over baseline systems
Human gesture classification by brute-force machine learning for exergaming in physiotherapy
In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods
Learning Temporal Alignment Uncertainty for Efficient Event Detection
In this paper we tackle the problem of efficient video event detection. We
argue that linear detection functions should be preferred in this regard due to
their scalability and efficiency during estimation and evaluation. A popular
approach in this regard is to represent a sequence using a bag of words (BOW)
representation due to its: (i) fixed dimensionality irrespective of the
sequence length, and (ii) its ability to compactly model the statistics in the
sequence. A drawback to the BOW representation, however, is the intrinsic
destruction of the temporal ordering information. In this paper we propose a
new representation that leverages the uncertainty in relative temporal
alignments between pairs of sequences while not destroying temporal ordering.
Our representation, like BOW, is of a fixed dimensionality making it easily
integrated with a linear detection function. Extensive experiments on CK+,
6DMG, and UvA-NEMO databases show significant performance improvements across
both isolated and continuous event detection tasks.Comment: Appeared in DICTA 2015, 8 page
Down-Sampling coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
In the field of gestural action recognition, many studies have focused on
dimensionality reduction along the spatial axis, to reduce both the variability
of gestural sequences expressed in the reduced space, and the computational
complexity of their processing. It is noticeable that very few of these methods
have explicitly addressed the dimensionality reduction along the time axis.
This is however a major issue with regard to the use of elastic distances
characterized by a quadratic complexity. To partially fill this apparent gap,
we present in this paper an approach based on temporal down-sampling associated
to elastic kernel machine learning. We experimentally show, on two data sets
that are widely referenced in the domain of human gesture recognition, and very
different in terms of quality of motion capture, that it is possible to
significantly reduce the number of skeleton frames while maintaining a good
recognition rate. The method proves to give satisfactory results at a level
currently reached by state-of-the-art methods on these data sets. The
computational complexity reduction makes this approach eligible for real-time
applications.Comment: ICPR 2014, International Conference on Pattern Recognition, Stockholm
: Sweden (2014
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