8,764 research outputs found
Local depth patterns for fine-grained activity recognition in depth videos
© 2016 IEEE. Fine-grained activities are human activities involving small objects and small movements. Automatic recognition of such activities can prove useful for many applications, including detailed diarization of meetings and training sessions, assistive human-computer interaction and robotics interfaces. Existing approaches to fine-grained activity recognition typically leverage the combined use of multiple sensors including cameras, RFID tags, gyroscopes and accelerometers borne by the monitored people and target objects. Although effective, the downside of these solutions is that they require minute instrumentation of the environment that is intrusive and hard to scale. To this end, this paper investigates fine-grained activity recognition in a kitchen setting by solely using a depth camera. The primary contribution of this work is an aggregated depth descriptor that effectively captures the shape of the objects and the actors. Experimental results over the challenging '50 Salads' dataset of kitchen activities show an accuracy comparable to that of a state-of-the-art approach based on multiple sensors, thereby validating a less intrusive and more practical way of monitoring fine-grained activities
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding
performance and demonstrated the effectiveness of 3D representation for action
recognition. The existing depth-based and RGB+D-based action recognition
benchmarks have a number of limitations, including the lack of large-scale
training samples, realistic number of distinct class categories, diversity in
camera views, varied environmental conditions, and variety of human subjects.
In this work, we introduce a large-scale dataset for RGB+D human action
recognition, which is collected from 106 distinct subjects and contains more
than 114 thousand video samples and 8 million frames. This dataset contains 120
different action classes including daily, mutual, and health-related
activities. We evaluate the performance of a series of existing 3D activity
analysis methods on this dataset, and show the advantage of applying deep
learning methods for 3D-based human action recognition. Furthermore, we
investigate a novel one-shot 3D activity recognition problem on our dataset,
and a simple yet effective Action-Part Semantic Relevance-aware (APSR)
framework is proposed for this task, which yields promising results for
recognition of the novel action classes. We believe the introduction of this
large-scale dataset will enable the community to apply, adapt, and develop
various data-hungry learning techniques for depth-based and RGB+D-based human
activity understanding. [The dataset is available at:
http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
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
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
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of such a fine-grained representation based on elementary
poses is very tedious and does not scale. Therefore, we propose a completely
unsupervised deep learning procedure based solely on video sequences, which
starts from scratch without requiring pre-trained networks, predefined body
models, or keypoints. A combinatorial sequence matching algorithm proposes
relations between frames from subsets of the training data, while a CNN is
reconciling the transitivity conflicts of the different subsets to learn a
single concerted pose embedding despite changes in appearance across sequences.
Without any manual annotation, the model learns a structured representation of
postures and their temporal development. The model not only enables retrieval
of similar postures but also temporal super-resolution. Additionally, based on
a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201
- …