112,826 research outputs found
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
In this paper we propose a novel approach to multi-action recognition that
performs joint segmentation and classification. This approach models each
action using a Gaussian mixture using robust low-dimensional action features.
Segmentation is achieved by performing classification on overlapping temporal
windows, which are then merged to produce the final result. This approach is
considerably less complicated than previous methods which use dynamic
programming or computationally expensive hidden Markov models (HMMs). Initial
experiments on a stitched version of the KTH dataset show that the proposed
approach achieves an accuracy of 78.3%, outperforming a recent HMM-based
approach which obtained 71.2%
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Recently, Long Short-Term Memory (LSTM) has become a popular choice to model
individual dynamics for single-person action recognition due to its ability of
modeling the temporal information in various ranges of dynamic contexts.
However, existing RNN models only focus on capturing the temporal dynamics of
the person-person interactions by naively combining the activity dynamics of
individuals or modeling them as a whole. This neglects the inter-related
dynamics of how person-person interactions change over time. To this end, we
propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to
model the long-term inter-related dynamics between two interacting people on
the bounding boxes covering people. Specifically, for each frame, two
sub-memory units store individual motion information, while a concurrent LSTM
unit selectively integrates and stores inter-related motion information between
interacting people from these two sub-memory units via a new co-memory cell.
Experimental results on the BIT and UT datasets show the superiority of
Co-LSTSM compared with the state-of-the-art methods
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for
micro-expression smile amplification along with three normalization procedures
for distinguishing posed and spontaneous smiles. Although the deep CNN face
model is trained with large number of face images, HOG features outperforms
this model for overall face smile classification task. Using EVM to amplify
micro-expressions did not have a significant impact on classification accuracy,
while the normalizing facial features improved classification accuracy. Unlike
many manual or semi-automatic methodologies, our approach aims to automatically
classify all smiles into either `spontaneous' or `posed' categories, by using
support vector machines (SVM). Experimental results on large UvA-NEMO smile
database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial
Behavior Analysi
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
Human action recognition in 3D skeleton sequences has attracted a lot of
research attention. Recently, Long Short-Term Memory (LSTM) networks have shown
promising performance in this task due to their strengths in modeling the
dependencies and dynamics in sequential data. As not all skeletal joints are
informative for action recognition, and the irrelevant joints often bring noise
which can degrade the performance, we need to pay more attention to the
informative ones. However, the original LSTM network does not have explicit
attention ability. In this paper, we propose a new class of LSTM network,
Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action
recognition. This network is capable of selectively focusing on the informative
joints in each frame of each skeleton sequence by using a global context memory
cell. To further improve the attention capability of our network, we also
introduce a recurrent attention mechanism, with which the attention performance
of the network can be enhanced progressively. Moreover, we propose a stepwise
training scheme in order to train our network effectively. Our approach
achieves state-of-the-art performance on five challenging benchmark datasets
for skeleton based action recognition
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