Automatic Car Detection Using Haar Cascade Classifier and Convolutional Neural Network for Traffic Density Estimation

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.

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.

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.

Automated facial emotion recognition (AFER) is a technique with rising usage across a range of practical real-world applications ranging from security to advertising. AFER can be used to assess the emotional state of patients with mental health conditions, such as depression or anxiety to guide treatment decisions. Companies can use facial emotion recognition to gauge consumer reactions to different products or advertisements, providing valuable insights for product development and marketing strategies. The following paper examines the concept of facial emotion recognition using AI-based models and compares the results of two different techniques. The first technique uses face emotion detection using haar cascade classifier along with convolutional neural networks (CNN), and the second technique uses face emotion detection using facial landmarks along with CNN.

Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data are acquired using aggressive Cartesian undersampling. First, we show that when each 2-D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2-D compressed sensing approaches, such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Second, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10 s and, for the 2-D case, each image frame can be reconstructed in 23 ms, enabling real-time applications.

Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with ( a) limited prediction accuracy ( b) decision-making latency, and ( c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic network state, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates network state estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway network. The results show that the effectiveness of the traffic management system can be affected by the deficiencies. However, the impact could be smaller if these deficiencies are kept under certain levels.

In this paper, we propose a low-cost, high-efficiency hardware architecture for a smart traffic light system. Our design especially achieves high efficiency for traffic systems where motorbikes are used as the main means of transportation. The main contributions of the paper consist of: (1) proposing a simple approach for automatically adjusting the green light time based on the state of the optical transmitter-receiver sensor pairs, (2) proposing a new traffic light signal generation, i.e., all traffic signals including the light signals and countdown timer module will be integrated inside only one block by using addressable WS2812B LEDs, (3) proposing the hardware architecture for a smart traffic light system which saves energy, maintenance costs, and is installed and operated easily.

The fundamental aim of this research is to develop a face detection system for a quadcopter in order to follow the face object. This research has two main stages, namely the face detection stage and the position control system. The face detection algorithm used in this research is the haar cascade method which is run using the python and OpenCV programming languages. The algorithm worked well, getting around 16fps on a low spec computer without a GPU unit. The results of the face detection algorithm are proven to be able to recognize faces from the camera installed on the DJI Tello mini drone. The mini drone was chosen because it is small and light, so it is harmless, and testing can be carried out indoors. Besides, the DJI Tello can be programmed easily using the python programming language. The drone’s position is then compared with the set point in the middle of the image to obtain errors so that control signals can be calculated for up/down, forward/backward, and right/left movements. From the testing results, the response speed that occurs in the right/left and up/down movements is less than 2 seconds, while for the forward/backward movement, it is less than 3 seconds.

Fusion of engineering insights and emerging trends: Intelligent urban traffic management system

This study aims to compare the effectiveness of two deep learning models, namely Convolutional Neural Network (CNN) and combined CNN with Gated Recurrent Unit (CNN-GRU), in predicting Petrobras stock prices. Using historical stock price data from Yahoo Finance for the period 2017-2023, this study evaluates the performance of both models based on several evaluation metrics. The results showed that the CNN-GRU model outperformed the pure CNN model in all evaluation metrics, with an increase in RMSE value of 4.17% and an increase in R² value of 0.47% on the test data. The CNN-GRU model achieved 96.14% accuracy on the test data, while the CNN model achieved 96.04%. These findings indicate that the integration of CNN's feature extraction capabilities with GRU's temporal dependency modeling capabilities can improve stock price prediction accuracy. This research contributes to the computational finance literature by presenting an in-depth comparative analysis of the application of hybrid deep learning architectures in stock market prediction.

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.

The steady increase in population and overcrowding has become an unavoidable factor in any public gathering or on the street during any festive occasions. The intelligent monitoring technology has been developing in recent years and human tracking has made a lot of progress. In this paper, we propose a method to manage the crowd by keeping in track the count of the people in the scene. In our study, we develop a system using Raspberry Pi 3 board that consists of ARMv8 CPU that detects the human heads and provide a count of humans in the region using OpenCV-Python. A Haar cascade classifier is trained for human head detection. Human tracking is achieved by indicating the direction of movement of the person. The results of the analysis will be helpful in managing the crowd in any area with high density of crowds.

In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc.

The goal of the project is to construct a density-based traffic signal management system that can automatically modify the timing of the signal based on the volume of traffic at the crossing. Traffic signal regulation is becoming a major issue that causes congestion and has a major role in the delay of emergency vehicles because of the rise in automobiles on the road these days. The majority of urban areas in the world have serious traffic problems, thus it's time to switch from more human or fixed modes to automated systems with decision-making skills. The fixed-based traffic signalling system of today may become ineffective if only one lane is used at a time. In this work, Arduino-based intelligent traffic control system framework helps to reduce this issue.

Smart Traffic Light Systems play an important role in urban traffic management. They often rely on cameras and sensors to collect traffic data. However, these methods are limited in terms of vehicle occupancy and queuing. Effective traffic management remains a challenge in urban areas owing to traffic congestion and emergencies. A new system called ADSTLS (Adaptive and Dynamic Smart Traffic Light System) is proposed, which handles traffic management at an intersection and effectively solves the cumbersome problem of traffic congestion while ensuring priority for emergency vehicles. ADSTLS provides fault tolerance to its components and works reliably in most failure situations. Therefore, traffic data is collected from cameras, and useful traffic information is extracted using computer vision and image processing. The proposed system also uses the Weight Chicken Swarm Optimisation algorithm for decision‐making to reduce congestion and average vehicle waiting time significantly. ADSTLS was applied to a real case study of EL‐Hidhab Setif city intersection. The approach's effectiveness was confirmed by thorough experiments, resulting in a noteworthy decrease in the average vehicle waiting time (31 s) and queue occupation rate (33.82%) across all simulated traffic scenarios. Furthermore, compared to other car types, emergency vehicles usually had much shorter wait times.