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As human action is uncertain and illegible, a human action recognition method basing on fuzzy support vector machine is presented. Fuzzy support vector machine employs the membership function to solve the unclassifiable areas which happens the traditional SVMs' two-class problems extend to the multi-class problems. the method is evaluated on the Weizmann action dataset and received comparative high...
During the past years, face and gait recognition in video have received significant attention. Consequently, their recognition problems have challenged due to largely varying appearances and highly complex pattern distributions. However, the complementary properties of these two biometrics suggest fusion of them. Face recognition is more reliable when the person is close to the camera. On the other...
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...
Human action recognition is a challenging filed in computer vision. In this paper, a novel probabilistic graphical model, called topic-relative conditional random field(TCRF), is firstly proposed. The model is constructed by adding a topic node and using a triangular-chain structure in the top layer of the linear-chain conditional random field(LCRF) to overcome the drawback of independent and identical...
This paper presents a unified action recognition framework combining harris3D descriptor with 3D SIFT detector. We perform action recognition experiments on the KTH dataset using Support Vector Machines. Experiments apply the leave-one-out and compare our proposed approach with state-of-the-art methods. The result shows that our proposed approach is effective. Compared with other approaches our approach...
The human experience in the analysis of the handwriting of male and female writers indicates that gender affects the appearance of the handwritten text. These differences are usually very difficult to describe numerically. In order to analyze the handwriting differences between male and female writers, several shape description techniques, such as the tangent angle function, curvature function and...
This paper presents a Nonlinear AutoRegressive with eXogenous input (NARX)-based approach for human-emotion recognition from an input video. The dynamics of facial expressions are first captured by performing a temporal-spatial analysis by extracting local and spatial features using a pyramid of histograms of oriented gradients (PHOG) descriptor. Then the temporal phases of facial expressions are...
There is good reason to believe that humans use some kind of recursive grammatical structure when we recognize and perform complex manipulation activities. We have built a system to automatically build a tree structure from observations of an actor performing such activities. The activity trees that result form a framework for search and understanding, tying action to language. We explore and evaluate...
In this paper, we propose an approach to detect scene geometrical structure given only one monocular image. Several typical scene geometries are investigated and corresponding models are built. A scene geometry reasoning system is set up based on image statistical features and scene geometric features. This system is able to find best fitting geometric models for most of the images from the benchmark...
Facial asymmetry is an important characteristic used in a number of applications. It plays a vital role in human perception of attractiveness and as such has been used in psychology including research on facial expressions evaluation as well as in plastic surgery and orthodontics. It has been also recognized as a biometric feature used for identification and has important applications in detection...
Facial expression has an important role for natural interaction among social robots and humans. In this paper, an architecture conceived for imitation of facial expressions is proposed. We describe the computer vision algorithm that was implemented for real-time geometric facial features extraction. It covers face detection, extraction of eyes, eyebrows, nostrils and mouth characteristic points, as...
This work investigates a semantic-driven human detection algorithm, which employs global human template matching to inspire the local features based Adaboosting algorithm. We use distance transform to analyze distances between training samples and human contour template to obtain a classifier based on human outline features. At the training stage, the global outline feature will be coordinated into...
Human Computation is defined as the integration of human tasks and automated algorithms to achieve superior quality in complex tasks like multimedia content analysis. This paper discusses a scenario in which human computation is used to segment time stamped fashion images for mining trends based on visual features of garments (e.g., color and texture) and attributes of portrayed subjects (e.g., gender...
Non-verbal human social signals have emerged as an important area of study including the analysis of human deception. The ability to credibly detect truth and deception can be critical today especially due to the wave of terrorism acts and illegal immigration upheavals just to mention a few instances where individuals might not be forthright with their information. Unlike for non-verbal human social...
This paper presents an empirical evidence of user bias within a laboratory-oriented evaluation of a Spoken Dialog System. Specifically, we addressed user bias in their satisfaction judgements. We question the reliability of this data for modeling user emotion, focusing on contentment and frustration in a spoken dialog system. This bias is detected through machine learning experiments that were conducted...
A main challenge facing the law-enforcement and intelligence-gathering environment is accurately and efficiently analyzing the huge volumes of data. Analyzing crime data can be difficult because of recognizing key features and transactions among the large amounts of data, of which only a small section is relevant to illegal process. An intelligent forensic system (IFS) is a powerful medium that enables...
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...
We present a robust system for large-scale abandoned object detection (AOD) with low false positive rates and good detection accuracy under complex realistic scenarios. The robustness of our system is largely attributed to an approach we develop for foreground analysis, which can effectively differentiate foreground objects from background under challenging conditions such as lighting changes, low...
This paper presents a new feature descriptor for real-time people detection in depth images. The shape cue in depth images can reduce negative impacts of variations of clothing, lighting conditions and the complexity of backgrounds. The proposed Simplified Local Ternary Patterns (SLTP) can take advantage of depth images to describe human body shape with low computational cost. To evaluate the SLTP...
Crowd counting and density estimation is still one of the important task in video surveillance. Usually a regression based method is used to estimate the number of people from a sequence of images. In this paper we investigate to estimate the count of people in a crowded scene. We detect the head region since this is the most visible part of the body in a crowded scene. The head detector is based...
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