Vehicle Tracking and Counting through Image Processing

This paper explores the feasibility of using a lowcost embedded ARM-based system for real-time vehicle detection, classification and counting through image processing algorithms with the aim of knowing information about vehicular traffic in different roads and highways to improve the management of mobility and the functioning of cities. This paper proposes the implementation of a low cost system to identify and classify vehicles using an Embedded ARM based platform (ODROID XU-4) with Ubuntu operating system. The algorithms used are based on the Open-source library (Intel OpenCV) and implemented in Python programming language. The experimentation carried out proved that the efficiency of the algorithm implemented was 95.35%, but it can be improved by increasing the training sample.

Traffic signal management is one of the major problematic issues in the current situation. Such scenarios, every signal are getting 60 seconds of timing on the road at a regular interval, even when traffic on that particular road is dense. As per this proposed model in this article, which will be optimized the timing interval of the traffic signal purely depends on the number of vehicles on that particular roadside. The major advantage of this system is that it can able to decrease the more waiting time for the drivers to cross road signal. In this model, we are using the clustering algorithms model which is based on KNN algorithm. Using this algorithm new model will be liable to determine expected required timing as per provided inputs to the signal which is vehicles count. The input of these systems is vehicles counts on each side of the road from crossing signal. And this input will be determined on much time is to be provided. “Case studies on this system are traffic network and real-time traffic sub-networks are organized to get the effectiveness of the proposed model.”

Computer vision has many applications that were implemented to the existing advance technology. Vehicle tracking and counting in video surveillance is one of them. Vehicle tracking allow the computer to detect and tracking the vehicles that capture through the video surveillance. Afterwards vehicle counting allow to the computer to count all of the vehicles on video. Background Subtraction is one of the method to perform object tracking and counting process for the system. This research implements Pixel Based Adaptive Segmenter Model for Background Subtraction method to perform object tracking and counting. This research used video capture of visual surveillance and vehicles as object to track and count. Result of this research is the system can track and count all vehicles on videos.

This research focuses on the development of a real-time vehicle detection and tracking system using OpenCV and deep learning models to address growing traffic management challenges. The project integrates the Gaussian Mixture-based Background Subtraction (MOG2), morphological operations, contour detection, and tracking methods like Kalman Filtering and Deep SORT. Additionally, object classification via YOLO and MobileNet-SSD significantly improves the accuracy of identifying and distinguishing vehicles. Evaluated on multiple real-world datasets under various environmental conditions, the system demonstrated strong performance with over 91% mAP, 92.5% precision, and robust multi-object tracking accuracy. This study serves as a scalable framework for intelligent traffic surveillance.

Aiming at vehicle detection and tracking problems in video monitoring and controlling system, this paper mainly studies vehicle detection and tracking problems in conditions of high traffic density in daytime. This paper is distinguished by two key contributions. First, we develop an improvement — SEAP(Simple but Efficient After Process) which checks the detection results in an accurate way and is an after process of Adaboost [1] detector which used to detect car in every frame. Second, we propose a tracking algorithm named 4-states tracking algorithm based on Kalman[5] linear filter. Tracking results turn unsteady as traffic density grows higher because of much more false positives and false negatives appear. However, 4-states tracking algorithm can solve this problem in an easy way by introducing FSM (Finite State Machine) into tracking algorithm. Finally, we implement a real-time vehicle detection and tracking system with the upper methods. Experiments give good results in relative crowded Conditions.

Vehicle counting and traffic volume estimation on traffic videos are challenging tasks for traffic monitoring and management. Most previous methods are based on a process of vehicle detection and tracking or a process of time-spatial image (TSI)-based background subtraction, which suffer from occlusion and small size of vehicles. To overcome these difficulties, a time-spatial-based structure is proposed for vehicle counting and traffic volume estimation, without relying on tracking and TSI-based background subtraction. This structure has three main parts. First, a TSI-based density map generation model is proposed for generating density maps for TSIs, which makes it possible to automatically generate TSI training samples. Second, the time-spatial multiscale net (TM-Net) is proposed for estimating density map of TSI; with the stacked multiscale modules and the spatial attention module, the proposed TM-Net can partly overcome the difficulties brought from occlusion, small size, and deformation. Finally, a vehicle counting and traffic volume estimation model is designed for counting and volume estimation on the results of TM-Net. Experiments performed on the public UA-DETRAC data set show that the proposed TM-Net-based vehicle counting method outperforms the tested representative counting methods, and the proposed framework also can estimate traffic volume efficiently.

In modern days, increasing weapon-related threats in public places have created an immediate need for intelligent surveillance systems to detect crime in real-time. Traditional surveillance systems have struggles with recognizing small objects, occlusion, and the time it takes to respond, which makes them ineffective in crowded and fast-changing situations. To overcome these challenges, the suggested system combines closed-circuit television (CCTV) surveillance cameras with advanced deep learning methods, image processing, and computer vision techniques for real-time crime prediction and prevention. This study proposes a hybrid deep learning framework that merges a Faster region convolutional neural network and Mask Region Convolutional Neural Network, named FMR-CNN. The novel approach FMR-CNN represents a significant advancement towards improving object recognition and segmentation of images and videos. It has been combined with YOLOv8 to increase the real-time detection speed and localization accuracy significantly. Such a combination enables the concurrent utilization of high-resolution spatial context information and rapid frame-wise predictions, thus making it well-suited for continuous video surveillance tasks. The model was trained and tested on a five labeled class annotated dataset, where MobileNetV3 features are extracted to simulate real-world surveillance conditions. Experimental results show the hybrid model attains detection accuracy of 98.7%, average precision (AP) of 90.1, and speed of 9.2 frames per second (FPS), and generalizes to varied lighting, occlusion, object scales, and reduced computational complexity, making it highly effective for crime prevention. Using these models benefits police departments and law enforcement agencies, as it allows them to detect criminal offenses earlier and avoid untoward situations.

Image process is one among most growing analysis space these days and currently it's greatly integrated with the medical and biotechnology field. Image process will be used to analyze different medical and MRI pictures to induce the abnormality within the image. In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images and experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE) proposes associate economical K-means clump algorithm underneath Morphological Image process (MIP). Medical Image segmentation deals with segmentation of tumour in CT and MR pictures for improved quality in diagnosis. It is an important method and a difficult drawback because of noise presence in input pictures throughout image analysis. It's required for applications involving estimation of the boundary of associate object, classification of tissue abnormalities, form analysis, contour detection. Segmentation determines because the method of dividing associate image into disjoint consistent regions of a medical image. The amount of resources needed to explain massive set of information is simplified and is chosen for tissue segmentation. In our paper, this segmentation is administrated victimization K-means clump algorithm for higher performance. This enhances the growth boundaries more and is extremely fast in comparison to several other clustering algorithms. This paper produces the reliable results that area unit less sensitive to error. Keywords: Magnetic resonance image, Morphological Image processing, Image segmentation, K-means, Morphological operations, Fuzzy, PSNR, RMSE.

We propose a vehicle tracking algorithm that takes a new occlusion reasoning approach. We consider two different types of occlusions: explicit occlusion and implicit occlusion. We also propose a traffic flow extraction method with the velocity and trajectory of the moving vehicles. The proposed vehicle tracking system is composed of three parts: vehicle segmentation, vehicle tracking, and traffic parameter extraction. The vehicle segmentation part separates moving vehicles from their background. We employ the adaptive background approach, which does not update the background of moving objects. We also design a 2D token-based algorithm for vehicle tracking using Kalman filtering that has a modest amount of computational complexity. The traffic parameter extraction part estimates the traffic parameters, such as the vehicle count and the average speed. It also extracts the traffic flows. Finally, we have evaluated the proposed algorithm with some MPEG-7 test sequences.

Vehicle growth in Indonesia is not supported by the number of the road. This fact has caused traffic congestion easily occurred, especially in big cities. Intelligent Transportation System (ITS) is one of the solutions for this problem. In ITS, vehicle tracking and counting is a challenging issue for traffic surveillance. In this paper, Bhattacharyya distance-based tracking and vehicle counting is proposed. We propose a vehicle tracking method and vehicle counting system. We use Adaptive Background Learning with Hole Filling Algorithm to separate background and foreground in a frame. We detect objects in foreground region using contour of each object. Each detected object tracked using Bhattacharyya distance. Each tracked object is counted when the tracked object passed a predefined counting line. The proposed algorithm is evaluated by comparing tracking estimates with ground truth. We use precision and recall as metric to evaluate our tracking estimates with ground truth. Our vehicle counting result also evaluated by comparing to ground truth. Our tracking result have 96% recall in all experiment scenarios. Also, we have 1.3% and 7.4% error rate in our counting result compared to ground truth.

• Proposes a novel hybrid framework combining RGBD-based CNN feature extraction, LSTM temporal modeling, and reinforcement learning for real-time wrinkle detection and correction. • Integrates physics-based mass–spring simulation with adaptive stiffness and damping control to enhance fabric realism under dynamic conditions. • Achieves high classification accuracy of 96.4 % across shallow, medium, and deep wrinkles, with significant gains over 2D models. • Demonstrates robust wrinkle reconstruction under occlusion using Mask R-CNN–based segmentation and synthetic occlusion training. • Validates real-time performance at 30 FPS with low temporal drift (0.0187) and MSE (0.0246), enabling applications in virtual fashion, robotics, and AR/VR environments. This study presents a novel framework for real-time cloth wrinkle detection and optimisation, combining physics-based modelling with LSTM-Reinforcement Learning (LSTM-RL) and advanced computer vision techniques. A curated dataset of 45,876 annotated static garment images was used, featuring wrinkle attributes such as location, depth, width, and geometry. CNNs were employed for feature extraction, enhanced by Mask R-CNN to handle occlusions and RGBD data for depth-aware wrinkle modelling. A mass-spring system simulated fabric dynamics under environmental forces, while LSTM networks predicted the temporal evolution of wrinkles. Reinforcement learning dynamically adjusted fabric parameters, improving adaptability. The proposed RGBD model achieved a wrinkle detection accuracy of 96.4 %, outperforming the 2D model by over 9 %. Key metrics include an MSE of 0.0246, drift of 0.0187, and a reward value of -0.13543, with low policy and value loss confirming the RL agent’s learning stability. These results demonstrate high accuracy, real-time performance, and robustness, with strong implications for virtual fashion, robotics, and AR/VR applications.

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

This paper presents Fade, a vehicle detection and tracking system featuring monocular color vision and radar data fusion. Its main originality resides in its low-level fusion system. At each step and for each target, the fusion system fuses the results of four different image processing algorithms and radar information by automatically combining 12 different features and generating many possible target position proposals. It generates a belief network organized in three layers: sources, position proposals, and correlation between proposals. An inference algorithm is then used to find out the actual position of the target, by deducing which observations are wrong. The overall system runs in real time and has been evaluated on a variety of scenarios recorded in a real car using /sup RT/Maps. Fade yields very good detection results in most cases.

Image recognition and image processing usually contain the technique of image segmentation. Excellent segmentation results can directly affect the accuracy of image recognition and processing. The essence of image segmentation is to segment each frame of a certain image or a video into multiple specific objects or regions and represent them with different labels. This paper focuses on the segmentation results obtained in image segmentation of images used for intelligent monitoring of Mandarin exams are usually visualized for image analysis. In this paper, we first investigate the performance improvement techniques for semantic segmentation in the image segmentation task for intelligent monitoring of Mandarin exams, improve the pixel classification capability by performing semantic migration, and, for the first time, extend the dataset substantially by style transformation to improve the model’s recognition of advanced features. In addition, to further address the shortcomings of the dataset, this paper improves the performance of image segmentation using synthetic datasets by investigating synthetic dataset image segmentation improvement techniques that reduce the reliance on manually annotated datasets. Image segmentation techniques continue to advance, and there are even thousands of commonly used segmentation methods for image segmentation development to date. Among them, they can be broadly classified as region-based segmentation methods, threshold-based segmentation methods, edge-based segmentation methods, specific theory-based segmentation methods, and deep learning-based segmentation methods. However, the methods used in this paper have all been experimentally demonstrated to improve the effectiveness of the techniques and proved to outperform other existing methods in the same field in the publicly available datasets LSUN, Cityscapes, and GTA5 datasets, respectively.