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This article analyzes the impact of the introduction of robotics in industry today and its impact on workers and unions. It offers possible solutions to reduce or eliminate the rejection of human beings with the introduction of robots in the workplace, and concludes that learning and formation are the keys to do it.
Automated characterization of human actions plays an important role in video indexing and retrieval for many applications. Action change detection is considered among the most necessary element to ensure a good video description. However, it is quite challenging to achieve detection without prior knowledge or training. Usually humans are practicing different actions in the same video and their silhouettes...
We propose a nighttime pedestrian detection method for a moving vehicle equipped with a camera and the near-infrared lighting. The objects in the nighttime environment will reflect the infrared projected. In some cases, however, the clothes absorb most of the infrared and make the pedestrian partially invisible in that part. To deal with this, a part-based pedestrian detection method according to...
In this work we present a new way to analyze human facial expressions in social situations. We are able to record the smiling expressions of multiple subjects simultaneously using a wireless wearable device that records physiological signals and allows the subjects complete freedom of movement, both of head position and in their environment. We analize the correlation of facial expressions in two...
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...
CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face...
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...
Interactive training is a technique that allows humans to guide a learning algorithm. This technique is well suited to training first person shooter bots as it allows game designers to iterate a range of behaviors in real-time. This paper investigates an initial attempt at allowing users to interact with the learning process of a reinforcement learning algorithm to create first person shooter bot...
We investigate the use of human metrology for the prediction of certain soft biometrics, viz. gender and weight. In particular, we consider geometric measurements from the head, and those from the remaining parts of the human body, and analyze their potential in predicting gender and weight. For gender prediction, the proposed model results in a 0.7% misclassification rate using both body and head...
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,...
This paper proposes a new face detection method, combining AdaBoost algorithm and neural networks (NN). First, do a pretreatment of the human face image; then NN trains a series of weak classifiers, finally AdaBoost algorithm improves the accuracy of weak classifiers to achieve face detection. The experimental results show that AdaBoost-NN algorithm has better robustness and higher recognition rate,...
Due to the development of World Wide Web technologies, people are living in the place flooding trillions of web pages in every moment. The amount of web size has been increasing dramatically. For this reason, it is getting more difficult to find relevant web documents corresponding to what users want to read. Classifying documents into predefined categories is one of the most important tasks in Natural...
Several concepts from traditional research on Artificial Intelligence (AI) need to be trained before they can be used. For example, when applied to a computer game, its AI framework has to “learn” how the game should be played. However, such trainings may not be trivial due to the often complex game world environments. This paper presents a novel training approach for game AI frameworks where, instead...
Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this...
Pedestrian detection is an important part of intelligent transportation systems. In the literature, Histogram of Oriented Gradients (HOG) detector for pedestrian detection is known for its good performance, but there are still some false detections appearing in the cases with flat area or clustered background. To deal with these problems, in this research work we develop a new feature which is based...
Pedestrian detection is a major difficulty in the field of object detection. In order to achieve a balance between speed and accuracy, we propose a new framework in pedestrian detection based on HOG-PCA and Gentle AdaBoost. Firstly, each block-based feature of the image is encoded using the histograms of oriented gradients (HOG), then Principal Components Analysis (PCA) is used to reduce the dimensions...
Text Summarization is the process of identifying and extracting the most vital information in a document. It has been seen as an effective method for dealing with increasing amount of information on the Internet nowadays. In this paper, we present an application of Genetic Programming to the problem of Automatic Text Summarization. Genetic Programming was used to evolve the function that ranks the...
Prior to 9/11, the intelligence process and tools used by our government were primarily directed at known threats with well understood functions and activities. However, with the rising importance of non-state actors and asymmetric threats, threat-focused processes and tools are now directed at known threats whose functions and activities are not well understood [1]. Today's intelligence environment...
Higher Education faces a revolutionary opportunity for change created by the confluence of pressures from economic and societal changes along with opportunities for new modes of education offered by the advancement and widespread availability of Information and Communication Technology. Advances in learning technologies are creating opportunities to improve education in 21st century competencies...
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