A Statistical Framework for Real-Time Traffic Accident Recognition

Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traffic accident based on the Histogram of Flow Gradient (HFG) and statistical logistic regression analysis. First, optical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Finally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring applications.

On Improvement of Teaching Quality for a Selected Mathematical Topic Using Artificial Neural Networks Modeling(With a Case Study)

This paper motivated and inspired by an interdisciplinary critical educational issue adopted for a research work approach. It concerned with application of realistic Artificial Neural Networks (ANNs) models integrating reading brain function with multi-sensory cognitive learning theory. Specifically, these models adopted to improve tutoring quality (academic achievement) while teaching children “how to read?” considering the analysis and evaluation of phonics methodology. Herein, quantitative analysis and evaluation of this issue performed by considering two computer aided learning (CAL) packages concerned with a specific selected mathematical topic namely: long division process. Via realistic modeling of packages using (ANNs) based upon associative memory learning paradigm. In more details, at educational field practice; both CAL packages have been applied for teaching children algorithmic steps performing long division processes. Moreover, learning performance evaluation of presented packages considers children outcomes’ achievement after tutoring for suggested Mathematical Topic either with or without associated tutor’s voice. Interestingly, statistical analysis of obtained educational case study results at children classrooms (for both applied packages) versus classical tutoring proved to be in well agreement with obtained after ANNs computer simulation results.

A Fast Statistical Approach for Human Activity Recognition

An essential part of any activity recognition system claiming be truly real-time is the ability to perform feature extraction in real-time. We present, in this paper, a quite simple and computationally tractable approach for real-time human activity recognition that is based on simple statistical features. These features are simple and relatively small, accordingly they are easy and fast to be calculated, and further form a relatively low-dimensional feature space in which classification can be carried out robustly. On the Weizmann publicly benchmark dataset, promising results (i.e. 97.8%) have been achieved, showing the effectiveness of the proposed approach compared to the-state-of-the-art. Furthermore, the approach is quite fast and thus can provide timing guarantees to real-time applications.

Face Detection and Localization in Color Images: An Efficient Neural Approach

Automatic face detection and localization is a key problem in many computer vision tasks. In this paper, a simple yet effective approach for detecting and locating human faces in color images is proposed. The contribution of this paper is twofold. First, a particular reference to face detection techniques along with a background to neural networks is given. Second, and maybe most importantly, an adaptive cubic-spline neural network is designed to be used to detect and locate human faces in uncontrolled environments. The experimental results conducted on our test set show the effectiveness of the proposed approach and it can compare favorably with other state-of-the-art approaches in the literature.

Automatic Arabic Document Classification Based on the HRWiTD Algorithm

The documents contain a large amount of valuable knowledge on various subjects and, more recently, documents on the Internet are available from various sources. Therefore, automatic, rapid and accurate classification of these documents with less human interaction has become necessary. In this paper, we introduce a new algorithm called the highest repetition of words in a text document (HRWiTD) to classify the automatic Arabic text. The corpus is divided into a train set and a test set to be applied to proposed classification technique. The train set is analyzed for learning and the learning data is stored in the Learning Dataset file. The category that contains the highest repetition for each word is assigned as a category for the word in Learning Dataset file. This file includes non-duplicate words with the value of higher repetition and categories and they get from all texts in the train set. For each text in the test set, the category of words is assigned to a specific category by using Learning Dataset file. The category that contains the largest number of words is assigned as the predicted category of the text. To evaluate the classification accuracy of the HRWiTD algorithm, the confusion matrix method is used. The HRWiTD algorithm has been applied to convergent samples from six categories of Arabic news at SPA (Saudi Press Agency). As a result, the accuracy of the HRWiTD algorithm is 86.84%. In addition, we used the same corpus with the most popular machine learning algorithms which are C5.0, KNN, SVM, NB and C4.5, and their results of classification accuracy are 52.86%, 52.38%, 51.90%, 51.90% and 30%, respectively. Thus, the HRWiTD algorithm gives better classification accuracy compared to the most popular machine learning algorithms on the selected domain.

Image-Based Methods for Interaction with Head-Worn Worker-Assistance Systems

In this paper, a mobile assistance-system is described which supports users in performing manual working tasks in the context of assembling complex products. The assistance system contains a head-worn display for the visualization of information relevant for the workflow as well as a video camera to acquire the scene. This paper is focused on the interaction of the user with this system and describes work in progress and initial results from an industrial application scenario. We present image-based methods for robust recognition of static and dynamic hand gestures in realtime. These methods are used for an intuitive interaction with the assistance-system. The segmentation of the hand based on color information builds the basis of feature extraction for static and dynamic gestures. For the static gestures, the activation of particular sensitive regions in the camera image by the user’s hand is used for interaction. An HMM classifier is used to extract dynamic gestures depending on motion parameters determined based on the optical flow in the camera image.