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This paper proposes a markerless video analytic system for quantifying body parts movement while lying. These movements include: hand, leg, both hand & leg and turning to left or right movements. Combination of pixel intensity and area difference of both segmented and the whole parts of each silhouette compared with the following silhouettes would provide a useful cue for detection of different...
Since depth measuring devices for real-world scenarios became available in the recent past, the use of 3d data now comes more in focus of human action recognition. Due to the increased amount of data it seems to be advisable to model the trajectory of every landmark in the context of all other landmarks which is commonly done by dimensionality reduction techniques like PCA. In this paper we present...
The emergence of various vision-based applications has led researchers to do research on human action recognition. Good feature extraction technique is one of the important factors in making an efficient action recognition system. In this work, we propose a technique which we called `pentagon profile' to represent the image for the human action interpretation task. This profile is generated by connecting...
In this paper, a new action recognition system is proposed, which employs 3D FAST corner detection in ROI, compact 3D descriptor to represent action information, and SOM to learn and recognize actions. Through detecting 3D FAST corners in ROI, action information of shape and motion can be obtained, and noise corners can be deleted at the same time. Furthermore, based on 3D HOG, we produce a simpler...
This paper presents a method for representing and recognizing human actions based on pose similarity. For pose representation, we extend Histogram of Oriented Gradients (HOG) with directional statistics to obtain a HOG based descriptor with a smaller dimension. Then a directional similarity measurement for the proposed descriptor is put forward to provide a measure consistent with human perception...
We propose a novel region-based method to recognize human actions by analyzing regions surrounding the human body, termed as negative space according to art theory, whereas other region-based approaches work with silhouette of the human body. We find that negative space provides sufficient information to describe each pose. It can also overcome some limitations of silhouette based methods such as...
A common approach to human action recognition is to use 2-D silhouettes in the space-time volume as a basis for further extraction of useful features. In this paper, we present a novel motion representation based on difference images. We show that this representation exploits the dynamics of motion, and show its effectiveness in action recognition. Moreover, experimental results demonstrate that this...
This paper proposes a novel human action recognition approach which represents each video sequence by a cumulative skeletonized images (called CSI) in one action cycle. Normalized-polar histogram corresponding to each CSI is computed. That is the number of pixels in CSI which is located in the certain distance and angles of the normalized circle. Using hierarchical classification in two levels, human...
Human action recognition from video clips has received increasing attention in recent years. This paper proposes a simple yet effective method for the problem of action recognition. The method aims to encode human actions using the quantized vocabulary of averaged silhouettes that are derived from space-time windowed shapes and implicitly capture local temporal motion as well as global body shape...
This paper presents a unified framework for human action classification and localization in video using structured learning of local space-time features. Each human action class is represented by a set of its own compact set of local patches. In our approach, we first use a discriminative hierarchical Bayesian classifier to select those space-time interest points that are constructive for each particular...
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...
Action recognition is an important research issue in intelligent surveillance and many other automatic video systems. In this paper, we describe a novel method for the human action recognition from its silhouette in the video. In the algorithm, diffusion maps is used for dimensionality reduction as well as to preserve much of the geometrical structure. A global geometry and local temporal similarity...
This paper presents a method to recognize human actions from sequences of depth maps. Specifically, we employ an action graph to model explicitly the dynamics of the actions and a bag of 3D points to characterize a set of salient postures that correspond to the nodes in the action graph. In addition, we propose a simple, but effective projection based sampling scheme to sample the bag of 3D points...
This paper proposes a novel neural network approach for human action recognition based on Self Organizing Map (SOM). The SOM acts as a tool to cluster feature data and to reduce data dimensionality. The key poses in action sequences are extracted by the trained SOM. After the mapping of SOM, a human action sequence is represented as a trajectory of map units. For action recognition, a longest common...
In this paper, we propose spatio-temporal silhouette representations, called silhouette energy image (SEI) and silhouette history image (SHI) to characterize motion and shape properties for recognition of human movements such as human actions, activities in daily life. The SEI and SHI are constructed by using the silhouette image sequence of an action. The span or difference of the end time and start...
This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a sequence of an action into a Motion History Image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting...
We present a compact representation for human action recognition in videos using line and optical flow histograms. We introduce a new shape descriptor based on the distribution of lines which are fitted to boundaries of human figures. By using an entropy-based approach, we apply feature selection to densify our feature representation, thus, minimizing classification time without degrading accuracy...
In this paper, we present a temporal-state shape context (TSSC) method that exploits space-time shape variations for human action recognition. In our method, the silhouettes of objects in a video clip are organized into three temporal states. These states are defined by fuzzy time intervals, which can lessen the degradation of recognition performance caused by time warping effects. The TSSC features...
Human key posture extraction from videos will benefit video storage, video retrieval, human action recognition, human behaviour understanding and so on. This paper presents an approach to select key postures from human action sequences using 2D information. There are two steps in the proposed method. Information measurement which is a kind of global feature of a frame is used to roughly find key posture...
This paper proposes a boosting EigenActions algorithm for human action categorization. In determining the EigenActions, a spatio-temporal information saliency is first calculated from the video sequence by estimating pixel density function. Since human action can be approximated as a periodic motion, salient action unit, which is one cycle of the motion, is extracted and EigenActions are determined...
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