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Human action recognition is an important research area in the field of computer vision having a great number of real-world applications. This paper presents a multi-view action recognition framework that extracts human silhouette clues from different cameras, analyzes scene dynamics and interprets human behaviors by the integration of multivariate data in fuzzy rule-based system. Different features...
Human action recognition has been well studied recently, but recognizing the activities of more than three persons remains a challenging task. In this paper, we propose a motion trajectory based method to classify human group activities. Gaussian Processes are introduced to represent human motion trajectories from a probabilistic perspective to handle the variability of people's activities in group...
Recognizing human actions is of vital interest in video surveillance or ambient assisted living. We consider an action as a sequence of body poses which are themselves a linear combination of body parts. In an offline procedure, nonnegative tensor factorization is used to extract basis images that represent body parts. The weighting coefficients are obtained by filtering a frame with the set of basis...
Learning dynamical systems is one of the important problems in many fields. In this paper, we present an algorithm for learning non-linear dynamical systems which works by aligning local linear models, based on a probabilistic formulation of subspace identification. Because the procedure for constructing a state sequence in subspace identification can be interpreted as the CCA between past and future...
This paper presents a new method based on template recognition and fuzzy recognization. HDM (Hybrid Differential Method) is used to locate the teeth area from a face image, and fuzzy recognition is used to decide if a new teeth image belongs to the face image. 5-6 times of training is done to build up teeth feature set. A relative simple tooth feature-the lower part width of central incisor and their...
Existing action recognition approaches mainly rely on the discriminative power of individual local descriptors extracted from spatio-temporal interest points (STIP), while the geometric relationships among the local features are ignored. This paper presents new features, called pairwise features (PWF), which encode both the appearance and the spatio-temporal relations of the local features for action...
In order to achieve accurate recognition of human finger vein (FV), this paper addresses the problems of finger vein localization and vein feature extraction. An inherent physical property of human fingers is used to localize the region of interest (ROI) of vein images as well as removing uninformative vein imagery based on the inter-phalangeal joint prior. In addition, vein images are characterized...
Recognizing a person's motion is intuitive for humans but represents a challenging problem in machine vision. In this paper, we present a multi-disciplinary framework for recognizing human actions. We develop a novel descriptor, the Human Action Image (HAI): a physically-significant, compact representation for the motion of a person, which we derive from first principles in physics using Hamilton's...
In this paper, we present a method that recognizes single or multiple common actions between a pair of video sequences. We establish an energy function that evaluates geometric and photometric consistency, and solve the action recognition problem by optimizing the energy function. The proposed stochastic inference algorithm based on the Monte Carlo method explores the video pair from the local spatio-temporal...
In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.). Hence, we use...
Human action recognition is often addressed by use of latent-state models such as the hidden Markov model and similar graphical models. As such models require Expectation-Maximisation training, arbitrary choices must be made for training initialisation, with major impact on the final recognition accuracy. In this paper, we propose a histogram-based deterministic initialisation and compare it with...
In this paper, we explore the idea of using only pose, without utilizing any temporal information, for human action recognition. In contrast to the other studies using complex action representations, we propose a simple method, which relies on extracting “key poses” from action sequences. Our contribution is two-fold. Firstly, representing the pose in a frame as a collection of line-pairs, we propose...
The paper presents a novel human motion recognition method based on a new form of the Hidden Markov Models, called spatial-temporal hidden markov models (ST-HMM), which can be learnt from a sequence of joints positions. To cope with the high dimensionality of the pose space, in this paper, we exploit the spatial dependency between each pair of spatially connected joints in the articulated skeletal...
A novel algorithm for human action recognition in the transform domain is presented. This approach is based on Two-Dimensional Principal Component Analysis (2DPCA) and Vector Quantization (VQ). This technique reduces the computational complexity and the storage requirement by at least a factor of 45.27, and 12 respectively, while achieving the highest recognition accuracy, compared with the most recently...
As a temporal classification problem, visual-based human actions recognition is an important component for some potential applications. In this paper, we combine Fuzzy Principle Component Analysis(Fuzzy PCA) and hidden Conditional Random Fields(HCRFs) to achieve a viewpoint insensitive human action recognition. Fuzzy PCA is used to reduce the dimension of the silhouette image features to obtain the...
Recently as digital cameras and web cameras have been commonly used in our everyday lives, we have become able to easily obtain quite a few movies. However, there are some problems in terms of managing movies, for example, it is difficult to find particular scenes in a movie, etc. As a first step toward finding particular scenes, we focus on retrieving scenes where human activity is recorded and annotating...
Gait recognition has already achieved satisfactory performance on small databases under ideal conditions. Most of the existing approaches represent gait pattern using a locomotion model or statistic model of human silhouette. However, it is still a challenging task to conduct human gait identification under variations of clothing and carrying condition in real scenes. In this paper, an adaptive part-based...
Multi-person activity recognition is a challenging task due to the complex interactions between people and the multi-dimensionality of features. This paper proposes a hierarchical and observation decomposed hidden Markov model to classify multi-person activities. In order to give detailed descriptions of people's interactions by different feature scale, states of individual persons and states of interactions...
A 3-D UWB SAR has been designed to detect the positions of the targets behind the wall and identify them as well. The beam scanning is based on the mechanical movement of the wideband Tx/Rx Vivaldi array. The system transceiver and 3-D microwave beamforming algorithm are introduced in details. An experiment is performed to recover the 3-D images of a human mock-up. The demonstrated 3-D imaging results...
This paper proposes a simple and effective human action recognition algorithm based on projection and mass center movement features. Firstly, divide the original video into equal length subsequences with overlapping time window, and make moving parts detection using adjacent frame difference. Then, horizontal projection and vertical projection of binary image are made, and in order to get ride of...
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