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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...
In this paper, we propose a crowd density estimation algorithm based on multi-class Adaboost using spectral texture features. Conventional methods based on self-organizing maps have shown unsatisfactory performance in practical scenarios, and in particular, they have exhibited abrupt degradation in performance under special conditions of crowd densities. In order to address these problems, we have...
Biometric recognition based on the characteristics of human faces has attracted a great deal of attention over the past few years. However, the similarity in the facial appearance of identical twins has made the task difficult and has even compromised commercial face recognition systems. In this paper, we shed new light on the study of facial recognition of identical twins and propose a novel approach...
This research work proposes an innovative processing scheme for the exploitation of eye movement dynamics on the field of biometrical identification. As the mechanisms that derive eye movements highly depend on each person's idiosyncrasies, cues that reflect at a certain extent individual characteristics may be captured and subsequently deployed for the implementation of a robust identification system...
Most of current 3D face reconstruction methods based on 2D image are complex. In this paper, a novel and efficient method on the basis of geometric transformation is presented. The method is composed of two aspects, pose estimation and 3D model reconstruction. For the first part, an improved Active Shape Model(ASM) algorithm is used to detect the feature points of the face in the image, and the frontal...
This paper presents the use of design grammars to evolve playable 2D platform levels through grammatical evolution (GE). Representing levels using design grammars allows simple encoding of important level design constraints, and allows remarkably compact descriptions of large spaces of levels. The expressive range of the GE-based level generator is analyzed and quantitatively compared to other feature-based...
In managing multimedia services, it is important to understand how network performance affects user experience. The model presented in this paper aims to estimate user perception of video quality based on defect events, which are automatically classified by machine learning techniques. The underlying principle of our model is that human experience is event-based and there is a strong correlation between...
In this paper, we describe Object Pixel Mixture Classifiers (OPMCs) which classify an object not only apart from background but also from other objects based on Gaussian Mixture Model (GMM) classification. The proposed OPMC is different from general GMM based classifiers in the respect that novel pairwise threshold is applied for final classification. Pairwise thresholds are different thresholds depending...
This paper presents an ultrasonic sensing based human face identification approach. As a biometric identification method, ultrasonic sensing could detect the geometric structure of faces without being affected by the illumination of the environment. Multi ultrasonic sensors are used for data collection. Continuous Transmitted Frequency Modulated (CTFM) signal is chosen as the detection signal. High...
Detection and classification of significant human motions are important tasks when analyzing a video that records human activities. Among various human motions, we consider that repetitious motions are specially important since they are usually results of activities with clear intentions. In this paper, we propose and evaluate a method that detects video segments that contain repetitious motions,...
Human tracking is an important vision task in video surveillance and perceptual human-computer interfaces. This paper presents a novel algorithm for region-based human tracking using color and depth features. We propose an adaptive autoregressive logarithmic search (ARLS) to estimate the target position, and use depth information to further reduce the false alarm rate. The new ARLS algorithm is evaluated...
The efficient browsing and retrieval of videos is a fundamental part of current multimedia systems especially on mobile devices. One way to provide enjoyable video browsing to the users is by providing summaries of the videos whereby the users can have a clue of the contents of the video before watching the video itself. In this paper, we propose a key frame extraction based video summarization technique...
The bag-of-words approach with local spatio-temporal features have become a popular video representation for action recognition. Recent methods have typically focused on capturing global and local statistics of features. However, existing approaches ignore relations between the features, particularly space-time arrangement of features, and thus may not be discriminative enough. Therefore, we propose...
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