Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

Hajj and Umrah are two main religious duties for Muslims.To help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each place.Since the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management process.In this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence techniques.Human detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security purposes.The proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and tracking.The main contribution of this work focuses on the detection and tracking model which is based on an improved object detection model.The improved Yolo v4 was used for face detection and tracking.It has been very effective in detecting small objects in highresolution images.The novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired task.Channel wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building blocks.The main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive proposals.We demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available dataset.The wider faces dataset was used for the train and the evaluation of the proposed detection and tracking model.The proposed model has achieved good results with 91.2%of mAP and a processing speed of 18 FPS on the Nvidia GTX 960 GPU.

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.

Desertification Detection in Makkah Region based on Aerial Images Classification

Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi Arabia.Makkah is one of the most important cities in Saudi Arabia that needs to be protected from desertification.The vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate change.The damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation area.In this paper,we propose an automatic desertification detection system based on Deep Learning techniques.Aerial images are classified using Convolutional Neural Networks(CNN)to detect land state variation in real-time.CNNs have been widely used for computer vision applications,such as image classification,image segmentation,and quality enhancement.The proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded implementation.It has achieved high efficiency with 96.47% accuracy.In light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.

To Perform Road Signs Recognition for Autonomous Vehicles Using Cascaded Deep Learning Pipeline

Autonomous vehicle is a vehicle that can guide itself without human conduction.It is capable of sensing its environment and moving with little or no human input.This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving.Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths,as well as obstacles and relevant road signs.In this paper,we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs.The road signs classifier based on an artificial intelligence technique.In particular,a deep learning model is used,Convolutional Neural Networks(CNN).CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection.CNN has successfully used to solve computer vision problems because of its methodology in processing images that are similar to the human brain decision making.The evaluation of the proposed pipeline was trained and tested using two different datasets.The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8%and a testing accuracy of 99.6%.The proposed method can be easily implemented for real time application.

A Novel Dataset For Intelligent Indoor Object Detection Systems

Indoor Scene understanding and indoor objects detection is a complex high-level task for automated systems applied to natural environments.Indeed,such a task requires huge annotated indoor images to train and test intelligent computer vision applications.One of the challenging questions is to adopt and to enhance technologies to assist indoor navigation for visually impaired people(VIP)and thus improve their daily life quality.This paper presents a new labeled indoor object dataset elaborated with a goal of indoor object detection(useful for indoor localization and navigation tasks).This dataset consists of 8000 indoor images containing 16 different indoor landmark objects and classes.The originality of the annotations comes from two new facts taken into account:(1)the spatial relationships between objects present in the scene and(2)actions possible to apply to those objects(relationships between VIP and an object).This collected dataset presents many specifications and strengths as it presents various data under various lighting conditions and complex image background to ensure more robustness when training and testing objects detectors.The proposed dataset,ready for use,provides 16 vital indoor object classes in order to contribute for indoor assistance navigation for VIP.

Contribution of the FPGAs for Complex Control Algorithms: Sensorless DTFC with an EKF of an Induction Motor

In a conventional direct torque control(CDTC) of the induction motor drive, the electromagnetic torque and the stator flux are characterized by high ripples. In order to reduce the undesired ripples, several methods are used in the literature. Nevertheless,these methods increase the algorithm complexity and dependency on the machine parameters such as the space vector modulation(SVM). The fuzzy logic control method is utilized in this work to decrease these ripples. Moreover, to eliminate the mechanical sensor the extended kalman filter(EKF) is used, in order to reduce the cost of the system and the rate of maintenance. Furthermore, in the domain of controlling the real-time induction motor drives, two principal digital devices are used such as the hardware(FPGA) and the digital signal processing(DSP). The latter is a software solution featured by a sequential processing that increases the execution time. However, the FPGA is featured by a high processing speed because of its parallel processing. Therefore, using the FPGA it is possible to implement complex algorithms with low execution time and to enhance the control bandwidth. The large bandwidth is the key issue to increase the system performances. This paper presents the interest of utilizing the FPGAs to implement complex control algorithms of electrical systems in real time. The suggested sensorless direct torque control using the fuzzy logic(DTFC) of an induction motor is successfully designed and implemented on an FPGA Virtex 5 using xilinx system generator. The simulation and implementation results show proposed approach s performances in terms of ripples, stator current harmonic waves, execution time, and short design time.

A Partitioning Methodology That Optimizes the Communication Cost for Reconfigurable Computing Systems

This paper focuses on the design process for reconfigurable architecture. Our contribution focuses on introducing a new temporal partitioning algorithm. Our algorithm is based on typical mathematic flow to solve the temporal partitioning problem. This algorithm optimizes the transfer of data required between design partitions and the reconfiguration overhead. Results show that our algorithm considerably decreases the communication cost and the latency compared with other well known algorithms.