9,030 research outputs found
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
Recently, mid-level features have shown promising performance in computer
vision. Mid-level features learned by incorporating class-level information are
potentially more discriminative than traditional low-level local features. In
this paper, an effective method is proposed to extract mid-level features from
Kinect skeletons for 3D human action recognition. Firstly, the orientations of
limbs connected by two skeleton joints are computed and each orientation is
encoded into one of the 27 states indicating the spatial relationship of the
joints. Secondly, limbs are combined into parts and the limb's states are
mapped into part states. Finally, frequent pattern mining is employed to mine
the most frequent and relevant (discriminative, representative and
non-redundant) states of parts in continuous several frames. These parts are
referred to as Frequent Local Parts or FLPs. The FLPs allow us to build
powerful bag-of-FLP-based action representation. This new representation yields
state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D
A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset
This paper aims to determine which is the best human action recognition
method based on features extracted from RGB-D devices, such as the Microsoft
Kinect. A review of all the papers that make reference to MSR Action3D, the
most used dataset that includes depth information acquired from a RGB-D device,
has been performed. We found that the validation method used by each work
differs from the others. So, a direct comparison among works cannot be made.
However, almost all the works present their results comparing them without
taking into account this issue. Therefore, we present different rankings
according to the methodology used for the validation in orden to clarify the
existing confusion.Comment: 16 pages and 7 table
An original framework for understanding human actions and body language by using deep neural networks
The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour.
By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way.
These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively.
While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements;
both are essential tasks in many computer vision applications, including event recognition, and video surveillance.
In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided.
The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements.
All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods
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