10,457 research outputs found
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
Log-Euclidean Bag of Words for Human Action Recognition
Representing videos by densely extracted local space-time features has
recently become a popular approach for analysing actions. In this paper, we
tackle the problem of categorising human actions by devising Bag of Words (BoW)
models based on covariance matrices of spatio-temporal features, with the
features formed from histograms of optical flow. Since covariance matrices form
a special type of Riemannian manifold, the space of Symmetric Positive Definite
(SPD) matrices, non-Euclidean geometry should be taken into account while
discriminating between covariance matrices. To this end, we propose to embed
SPD manifolds to Euclidean spaces via a diffeomorphism and extend the BoW
approach to its Riemannian version. The proposed BoW approach takes into
account the manifold geometry of SPD matrices during the generation of the
codebook and histograms. Experiments on challenging human action datasets show
that the proposed method obtains notable improvements in discrimination
accuracy, in comparison to several state-of-the-art methods
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Action Classification with Locality-constrained Linear Coding
We propose an action classification algorithm which uses Locality-constrained
Linear Coding (LLC) to capture discriminative information of human body
variations in each spatiotemporal subsequence of a video sequence. Our proposed
method divides the input video into equally spaced overlapping spatiotemporal
subsequences, each of which is decomposed into blocks and then cells. We use
the Histogram of Oriented Gradient (HOG3D) feature to encode the information in
each cell. We justify the use of LLC for encoding the block descriptor by
demonstrating its superiority over Sparse Coding (SC). Our sequence descriptor
is obtained via a logistic regression classifier with L2 regularization. We
evaluate and compare our algorithm with ten state-of-the-art algorithms on five
benchmark datasets. Experimental results show that, on average, our algorithm
gives better accuracy than these ten algorithms.Comment: ICPR 201
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions
3D action recognition has broad applications in human-computer interaction
and intelligent surveillance. However, recognizing similar actions remains
challenging since previous literature fails to capture motion and shape cues
effectively from noisy depth data. In this paper, we propose a novel two-layer
Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and
jointly encodes both motion and shape cues. First, background clutter is
removed by a background modeling method that is designed for depth data. Then,
motion and shape cues are jointly used to generate robust and distinctive
spatial-temporal interest points (STIPs): motion-based STIPs and shape-based
STIPs. In the first layer of our model, a multi-scale 3D local steering kernel
(M3DLSK) descriptor is proposed to describe local appearances of cuboids around
motion-based STIPs. In the second layer, a spatial-temporal vector (STV)
descriptor is proposed to describe the spatial-temporal distributions of
shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape
cues are combined to form a fused action representation. Our model performs
favorably compared with common STIP detection and description methods. Thorough
experiments verify that our model is effective in distinguishing similar
actions and robust to background clutter, partial occlusions and pepper noise
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
table
Learned Spatio-Temporal Texture Descriptors for RGB-D Human Action Recognition
Due to the recent arrival of Kinect, action recognition with depth images has attracted researchers' wide attentions and various descriptors have been proposed, where Local Binary Patterns (LBP) texture descriptors possess the properties of appearance invariance. However, the LBP and its variants are most artificially-designed, demanding engineers' strong prior knowledge and not discriminative enough for recognition tasks. To this end, this paper develops compact spatio-temporal texture descriptors, i.e. 3D-compact LBP (3D-CLBP) and local depth patterns (3D-CLDP), for color and depth videos in the light of compact binary face descriptor learning in face recognition. Extensive experiments performed on three standard datasets, 3D Online Action, MSR Action Pairs and MSR Daily Activity 3D, demonstrate that our method is superior to most comparative methods in respects of performance and can capture spatial-temporal texture cues in videos
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