Crowd detection and management using cascade classifier on ARMv8 and OpenCV-Python

Background: Detection and tracking of humans in real time is a challenging and important field of research. Human detection has many applications in detection of human and video surveillance. Nowadays, quadcopters are used in different fields like research, military and law enforcement, commercial fields for navigation, searching, and areal imagery. Methods: In this paper, the primary objective of the system is to detect and locate people who got isolated in some areas due to natural disasters like floods and earthquake. It is carried out using a single-board computer Raspberry Pi (RPI), and the image processing part is carried out with the help of OpenCV. The detection and tracking of humans is done using quadcopter. Quadcopter is equipped with camera, the video capture is processed using RPI, and live streaming along with human detection is sent to the base station over the wireless network. Applications: It is used in effective human detection in real time video streaming. It helps the rescue team to locate people and to know their current situation and thus to take necessary actions including medication and evacuation. Development/future work: A prototype model for human detection and tracking using quadcopter is designed and developed. Better battery pack for longer flying time and use of 3G USB dongle for large area surveillance.

Obtaining accurate information on stratigraphic conditions and drilling status is necessary to ensure the safety of the drilling process and to guarantee the production of oil and gas. Sensing while drilling and intelligent monitoring technology, which employ multiple sensors and involve the use of intelligent algorithms, can be used to collect downhole information in situ to ensure safe, reliable, and efficient drilling and mining operations. These approaches are characterized by effective sensing and comprehensive utilization of drilling information through the integration of multi-sensor signals and intelligent algorithms, a core component of machine learning. The article summarizes the current research status of domestic and international sensing while drilling and intelligent monitoring technology using systematically collected relevant information. Specifically, first, the drilling-sensing methods used for in situ acquisition of downhole information, including fiber-optic sensing, electronic-nose sensing, drilling engineering-parameter sensing, drilling mud-parameter sensing, drilling acoustic logging, drilling electromagnetic wave logging, and drilling seismic logging, are described. Next, the basic composition and development direction of each sensing technology are analyzed. Subsequently, the application of intelligent monitoring technology based on machine learning in various aspects of drilling- and mining-status identification, including bit wear monitoring, stuck drill real-time monitoring, well surge real-time monitoring, and real-time monitoring of oil and gas output, is introduced. Finally, the potential applications of sensing while drilling and intelligent monitoring technology in deep-earth, deep-sea, and deep-space contexts are discussed, and the challenges, constraints, and development trends are summarized.

Based on a survey released by the TomTom Traffic Index in 2018, Indonesia was ranked seventh in the category of the most congested country in the world. One of the factors affecting traffic congestion in Indonesia is an inflexible and conventional traffic management system. In this regard, it is necessary to have a better traffic management system such as a Smart Traffic Light. One way to implement a smart traffic light system is to make a vehicle detection and counting system on the traffic CCTV video automatically. The methods used in this research are Haar Cascade Classifiers and Convolutional Neural Network. Haar Cascade Classifiers have fast computation processes and CNN is applied to validate the detection results of the Haar Cascade method for better accuracy. The average level of accuracy achieved by the system on quiet test data is 82%, normal test data is 69%, and busy test data is 60%. Meanwhile, the average computation time needed by the system for the quiet test data is 0.63 seconds, the normal test data is 0.52 seconds, and the busy test data is 1.05 seconds.

Emergency Medical Services (EMS) require rapid response and efficient routing to ensure timely patient care. However, urban traffic congestion and static routing methods often delay ambulance arrivals. To addressthis, this paper proposes an intelligent ambulance routing and traffic-signal preemption framework, termed SLnO-CC, integrating Sea Lion Optimization (SLnO) for optimal route planning and a Haar Cascade Classifier (CC) for real-time emergency vehicle detection and signal control. The proposed model wasevaluated across eight real-world traffic scenarios within a 15 km urban area, benchmarking against A*, Advanced A* with Dispersion Index, Ant Colony Optimization (ACO), and standalone SLnO. Experimental results demonstrate that SLnO-CC achieved the lowest average response time (9.06 min) and travel time(5.36 min), outperforming A* (9.70 min, 12.20 min) and ACO (9.44 min, 11.47 min) by 6.6% and 13.2%, respectively. In terms of total routing efficiency, SLnO-CC reduced the overall distance and time by 17.8% and 19.6%, respectively, compared with existing baselines. The Haar Cascade–based preemption module achieved 96.8% detection accuracy under varying illumination and occlusion. Overall, the SLnO-CC framework enhances routing adaptability, congestion awareness, and emergency responsiveness—ensuring total response time remains within 10 minutes over a 15 km operational range with high detection reliability.

Background. In recent years the deterioration of the demographic situation has been noted in most countries of the world that is due to the steady increase in population of arterial hypertension and the aging of the population. According to the statistical forecast of the UN by 2025, the number of people over 60 will exceed 1 billion which is 15% of the entire global population. In Ukraine, according to epidemiological studies, the prevalence of hypertension among circulatory system diseases in adults (18 years and older) is 46.8% and almost half of patients with circulatory system diseases have elevated blood pressure. In patients aged 65 years and older, the prevalence of hypertension varies in the range of 53-72%. It has long been proven that the risk of cardiovascular and cerebrovascular complications in hypertensive patients depends not only on the absolute blood pressure level but also on fluctuations in blood pressure over different periods that is blood pressure variability. Blood pressure is not a static parameter but rather undergoes continuous fluctuations over time due to the interaction between environmental factors and behaviour on the one hand and the internal regulatory mechanisms of the cardiovascular system on the other hand. Elevated blood pressure may indicate cardiovascular dysregulation and itself may be a cardiovascular risk factor associated with increased all-cause mortality and cardiovascular mortality, stroke, coronary artery disease, heart failure, end-stage renal disease and incidence of dementia. Aim. The study aimed to improve the system of prevention and diagnosis of elderly and senior hypertensive patients at the ambulatory-polyclinic stage due to the study of the contribution of blood pressure variability. Materials and methods. The group of examinees was formed taking into account 27 elderly and senile hypertensive patients including those combined with coronary artery disease. For this purpose, ambulatory blood pressure monitoring was used. Results. Systolic blood pressure variability (SD) in hypertensive patients was higher than in the control group (p<0.001). So, during the study, SD was 17.9±7.1 mm Hg. while in the control group 12.1±2.6 mm Hg. The frequency of high blood pressure variability detection in elderly and senile hypertensive patients was 51.9% (n=14). High blood pressure variability compared to low blood pressure variability and control was associated with a more pronounced systolic blood pressure variability (during the active monitoring period), and it was equalled 23.7±5.2 mm Hg compared to 14.8±6.5 mm Hg (p<0.001) and 12.1±2.6 mm Hg. (p<0.001). In elderly and senile hypertensive patients with high and low blood pressure variability, the non-dipper group prevailed over the dipper group in the structure of daily blood pressure rhythm (71.4% vs. 28.6% and 84.6% vs. 15.4% respectively). According to the ambulatory blood pressure monitoring data high blood pressure variability group compared to low blood pressure variability and controls was characterised by higher average values of the number of blood pressure indicators. In the group of high blood pressure variability patients, the average daily systolic blood pressure exceeded the control value by 10% (р<0.05). The daily index of systolic blood pressure in the group of high blood pressure variability patients was 111% higher than in patients with low blood pressure variability (p<0.05). The daily index of diastolic blood pressure in the group of high blood pressure variability patients was 140% higher than in patients with low blood pressure variability (p<0.05). The daily index of average blood pressure in the group of high blood pressure variability patients was 191% higher than in patients with low blood pressure variability (p<0.01). Conclusions. 1. High blood pressure variability has occurred in 16 out of 27 (51.9%) hypertensive elderly and senile patients. 2. The non-dipper group has predominated in the structure of blood pressure diurnal rhythm in elderly and senile hypertensive patients with high and low blood pressure variability patients. _________________________________________________________________________________________ Keywords: arterial hypertension; essential hypertension; blood pressure variability; high blood pressure variability; low blood pressure variability; elderly and senile hypertensive patients

Food gaps problems occupies great importance in the light of the steady increase in population, so as the demand on major food commodities, and lack of resources. In this chapter, we presented the important results obtained in Chaps. 4, 5 and 6 to come with recommendations to policy makers.

Due to the lack of an effective policy of planning and prevention, over the past decades the area around Mt. Vesuvio has undergone a steady increase in population and uncontrolled housing development. Consequently, it has become one of the most hazardous volcanic areas in the world. In order to mitigate the damage that the impact of an explosive event would cause in the area, the Department of Civil Defense has worked out an Emergency Management Plan using the A.D. 1631 subplinian eruption as the most probable short-term event. However, from 25 000 years B.P. to present, the activity of the Somma-Vesuvio volcano has shown a sequence of eight eruptive cycles, which always began with a strong plinian eruption. In this paper we utilize the A.D. 79 eruption as an example of a potential large explosive eruption that might occur again at Vesuvio. A detailed tephrostratigraphic analysis of the eruption products was processed by a multivariate statistical analysis. This analysis proved useful for identifying marker layers in the sequences, thus allowing the recognition of some major phases of synchronous deposition and hence the definition of the chronological and spatial evolution of the eruption. By combining this reconstruction with land-use maps, a scenario is proposed with time intervals in the eruptive sequence similar to those reported in Pliny's letter. Thus, it was calculated that, after 7 h from the start of the eruption, a total area of approximately 300 km2 would be covered with the eruption products. In the following 11 h, a total area of approximately 500 km2 would be involved. The third and last phase of deposition would not cause significant variation in the total area involved, but it would bring about an increase in the thickness of the pyroclastic deposits in the perivolcanic area.

The phenomenon called “return to the city” has intensified in Rzeszów. After a clear decline in the number of inhabitants at the beginning of the present century, for several years there has been a steady increase in population, which is not related to incorporating more towns into the city. The paper presents the analysis of data collected during the study carried out in the city. Residents of the city declared satisfaction with their place of residence. The main reasons for satisfaction were the cleanliness of the city, aesthetics, work, noticeable city development, developed infrastructure, and a high level of security and peace.

The biometric-based attendance monitoring system replaces the manual method of taking attendance and reduces the time to take attendance and avoids proxy. There are many biometric methods developed based on the fingerprint recognition, DNA matching, face recognition, Iris recognition and voice recognition. Out of all, the methods based on face recognition are proved to be the best methods. In this paper we are presenting the implementation of attendance monitoring system based on the facial recognition. In this method the image database of the classroom is created first and the database is used to train the system. Then in recognition phase the camera is fixed at the focal point in the classroom and it will capture the images, then from the captured images student's faces are recognized & compared with the database and lastly the attendance is marked based on the faces identified. The marked attendance is directly reported to the students/parents and to the faculty handling the respective course. In this method, Haar cascade classifier is used to detect the faces and captured images are compared with the database by using local binary pattern histogram algorithm. The face recognition system is implemented on the Raspberry Pi 3B hardware.

In this paper, an improved method is proposed to detect falls using an uncalibrated camera. The proposed fall detection technique combines human shape analysis and human head detection together to detect falls from normal daily activities. The human shape is represented with an ellipse shape and features extracted from the ellipse are used to detect fall events. The head detection helps to distinguish between falls and fall-like incidents in the case where the activities of daily living at home happen to be parallel to the camera optical axis. Two novel approximate human head shape models are proposed to detect the head of the person. The experiment results demonstrate that this proposed method is able to achieve high detection accuracy compared to other methods in the literature.

This article examines using of iterative adaptation algorithms to solve the problem of determining measurement location of the carotid artery intima-media complex. The formulation of a multi-criteria decision-making problem, as the basis for determining correct criterion for proper selection and successful recognizing of the required object in an ultrasound image. The work discusses principles of constructing cascade classifiers as well as, the use of the cascade Haar classifier and the cascade LBP classifier, for which Haar primitives and local binary templates are used as a basis. The results of experimental studies in order to determine effectivity of different boosting algorithms to solve this problem are presented. The best results were shown by the Haar cascade classifier, developed using an iterative adaptation algorithm, which manages solving a multicriteria problem on a given training set more successfully and determines the most suitable areas for measuring the thickness of the common carotid artery intimate media complex.

Our world is going through a lot of misery due to the outbreak of coronavirus disease (COVID-19) from the end of 2019 affecting many people’s livelihood and causing deaths. Wearing a face mask has become very normal today as the whole world is trying to recover back to normalcy and continue their activities. Therefore, it has become crucial to implement this policy especially in public areas, offices, etc. Constant monitoring cannot be done on every individual. Hence, technology plays a prominent role for a steady implementation. The method consists of two stages of model creation using convolutional neural network (CNN) and model implementation using cascaded classifier (Haar cascade classifier). This model consists of an in-depth reading program. A rigid model is built using TensorFlow, Keras, and OpenCV. By varying the datasets and epoch numbers, we tend to maximize the accuracy and minimize the loss score. This model will be helpful for the authorities to track safety violations, promote the mask usage, and secure the work environment when integrated in various devices.

The paper proposes a self-driving car model also called autonomous, robotic or driver-less car is one that operates and navigates using its intelligence. The basic idea behind the paper is to develop a 1/10 scale RC car to portray an automated car. The model consists of the following software and hardware components such as CNN (Convolutional neural network), Monocular vision algorithm, Haar cascade classifier, Raspberry Pi Board model B+, Pi camera, Arduino, and an Ultrasonic sensor. The Pi camera and ultrasonic sensor are attached to the raspberry pi board to collect input images along with sensor data to stream these data to the server which in our case is the laptop. The (CNN) convolutional neural network running on the server will be used to enable lane detection to provide steering predictions that are left, right, forward based on the input image. The haar cascade classifier will be used to detect signals and stop sign and monocular vision algorithms to calculate distance from them. The ultrasonic sensor will be used for front collision avoidance by stopping the car at a certain distance before the obstacle ahead. The navigation commands such as right, left, forward, stop will be sent to the car through Arduino which is connected to the RC car's remote, this will make the car drive autonomously based on neural network predictions and some hard coded rules. Thus this model will enable autonomous driving by self-navigation via lane detection, stopping at detection of stop signs, red lights and moving on green signal and front collision avoidance in a cost-effective manner. Hardcoded rules, obstacle avoidance mechanisms, machine learning models, and smart object discrimination will help the system follow traffic rules and navigate the car using artificial intelligence.

Along with the strength of the country’s overall strength, the people’s pockets have become more and more popular, and there have been significant improvements in all aspects of life, especially in terms of travel methods. This reflects the increase in residents’ income, but it also brings huge traffic pressure. In the long run, traffic congestion is not only detrimental to urban development, but frequent traffic accidents threaten residents’ travel safety. Effective monitoring methods are essential to solving these problems, so it is necessary to carry out research on intelligent video monitoring technology in intelligent transportation. The purpose of this article is to solve the current situation of excessive traffic pressure in the city. Through the study of intelligent video surveillance technology in intelligent traffic, the use of constrained least squares algorithm to remove motion blur and apply Kalan filtering to the sharpening process is used to eliminate noise ambiguity and make a brief introduction to various classic moving target detection methods to realize real-time monitoring of intelligent traffic conditions and continuously adjust and verify the monitoring situation, and then establish intelligent video in intelligent traffic monitoring technology research system. The research results show that this kind of intelligent video surveillance technology research in intelligent transportation can effectively increase the awareness of intelligent video surveillance technology and improve the level of intelligent video surveillance technology. The data measurement time has been shortened by one hour, the aggregation time has been changed from three hours to two hours, and the analysis time has been shortened by half. Eased urban traffic road pressure and greatly reduced the incidence of traffic accidents, which is conducive to socialist harmony social construction.

The thesis addresses the following challenging problems of detecting and tracking humans in the presence of occlusions in typical surveillance videos: (1) adaptation of semantic-part-based human detectors to new surveillance video sequence when trained detectors using other video data not performing well on the new video data; (2) tracking of humans with person identification minimizing identification errors over longer tracking periods; and (3) hierarchical spatial and temporal analysis for discriminative tracking of human targets. The thesis aims to improve the state-of-the-art performance in human detection and tracking by studying the human detectors, extended tracking of track segments (tracklets) generated from short term tracking of detection responses. For the adaptation of semantic-part-based human detectors to new surveillance video sequence, a uni ed deep CNN model for joint learning of features, semantic pedestrian part detectors and a transfer learning model is developed. The components within this deep CNN model interact with each other in the learning process, which facilitates the optimization of the learned components during the co-operative learning. In particular, an adaptation layer is proposed to embed the capability of knowledge transfer into the CNN model. As a result, the proposed transferred CNN (T-CNN) model is able to transfer the visual knowledge of the semantic pedestrian parts from the source data to target data. Extensive experimental evaluations show that the proposed method is better than other deep learning based methods in terms of detection performance. Moreover, the adaptive deep features can be complementary to the pre-defined features used by other state-of-the-art methods. For tracking of humans with person identification minimizing identification errors over longer tracking periods, a novel method, based on online target-specific metric learning and coherent dynamics estimation, for tracklet association by network flow optimization is developed. The proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and also to recover missed detections. The target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e. during the tracking process. Furthermore, a learning algorithm to learn the weights of motion and appearance tracking cues for tracklet affinity models is proposed to handle some difficult situations. Extensive evaluations following state-of-the-art practices have been conducted and the results from these evaluations show the improvements by the proposed method over some existing state-of-the-art methods. In hierarchical spatial and temporal analysis for discriminative tracking of human targets, inspired by recent advances in convolutional neural network (CNN) architectures, a novel uni ed deep model for tracklet association, which can jointly learn the CNNs and temporally constrained metrics, is developed. Furthermore, a novel loss function incorporating temporally constrained multi-task learning mechanism is developed to make the deep model more effective in solving the tracklet association problem. Extensive experimental results comparing with the state-of-the-art methods demonstrate the effectiveness and superiority of the proposed unified deep model.