Kajian Literatur Metode Pendeteksian dan Pengenalan Kendaraan Berdasarkan Citra Digital

Pendeteksian dan pengenalan kendaraan menjadi topik yang menarik oleh para peneliti terutama di bidang visi komputer. Sistem pendeteksian dan pengenalan kendaraan secara otomatis dan real-time merupakan bagian penting pada Intelligent Transportation System (ITS). Pada makalah ini membahas beberapa kajian literatur tentang metode yang digunakan untuk pendeteksian dan pengenalan kendaraan. Kajian dilakukan dengan cara meninjau literatur yang berhubungan dengan pendeteksian dan pengenalan kendaraan menggunakan pendekatan image processing, baik dengan data masukan berupa citra maupun video. Hasil yang diharapkan dapat menjadi acuan untuk peneliti yang hendak melakukan penelitian tentang pendeteksian dan pengenalan kendaraan

A Vision Based Lane Marking Detection, Tracking and Vehicle Detection on Highways

Changing street conditions is an important issue in the applications in mechanized route of vehicles essentially because of vast change in appearance in lane markings on by variables such substantial movement and changing daylight conditions of the specific time of day. A path identification framework is an imperative segment of numerous computerized vehicle frameworks. In this paper, we address these issues through lane identification and vehicle recognition calculation to manage testing situations, for example, a lane end and flow, old lane markings, and path changes. Left and right lane limits will be distinguished independently to adequately handle blending and part paths utilizing a strong calculation. Vehicle discovery is another issue in computerized route of vehicles. Different vehicle discovery approaches have been actualized yet it is hard to locate a quick and trusty calculation for applications, for example, for vehicle crashing (hitting) cautioning or path evolving system .Vision-based vehicle recognition can likewise enhance the crash cautioning execution when it is consolidated with a lane marking identification calculation. In crash cautioning applications, it is vital to know whether the obstruction is in the same path with the sense of self vehicle or not

Confidence based active learning for vehicle classification in urban traffic

This paper presents a framework for confidence based active learning for vehicle classification in an urban traffic environment. Vehicles are automatically detected using an improved background subtraction algorithm using a Gaussian mixture model. A vehicle observation vector is constructed from measurement-based features and an intensity-based pyramid HOG. The output scores of a linear SVM classifier are accurately calibrated to probabilities using an interpolated dynamic bin width histogram. The confidence value of each sample is measured by its probabilities. Thus, only a small number of low confidence samples need to be identified and annotated according to their confidence. Compared to passive learning, the number of annotated samples needed for the training dataset can be reduced significantly, yielding a high accuracy classifier with low computational complexity and high efficiency. The detected vehicles are classified into four main categories: car, van, bus and motorcycle. Experimental results demonstrate the effectiveness and efficiency of our approach. The method is general enough so that it can be used in other classification problems and domains, e.g. pedestrian detection

Histograms of oriented gradients for fast on-board vehicle verification

Histograms of Oriented Gradients (HoGs) provide excellent results in object detection and verification. However, their demanding processing requirements bound their applicability in some critical real-time scenarios, such as for video-based on-board vehicle detection systems. In this work, an efficient HOG configuration for pose-based on-board vehicle verification is proposed, which alleviates both the processing requirements and required feature vector length without reducing classification performance. The impact on classification of some critical configuration and processing parameters is in depth analyzed to propose a baseline efficient descriptor. Based on the analysis of its cells contribution to classification, new view-dependent cell-configuration patterns are proposed, resulting in reduced descriptors which provide an excellent balance between performance and computational requirements, rendering higher verification rates than other works in the literature

Real-time Detection of Vehicles Using the Haar-like Features and Artificial Neuron Networks

AbstractIn this document, a vehicle detection system is presented. This system is based on two algorithms, a descriptor of the image type haar-like, and a classifier type artificial neuron networks. In order to ensure rapidity in the calculation extracts features by the descriptor the concept of the integral image is used for the representation of the image. The learning of the system is performed on a set of positive images (vehicles) and negative images (non-vehicle), and the test is done on another set of scenes (positive or negative). To address the performance of the proposed system by varying one element among the determining parameters which is the number of neurons in the hidden layer; the results obtained have shown that the proposed system is a fast and robust vehicle detector

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

A fast and self-adaptive on-line learning detection system

© 2018 The Authors. Published by Elsevier Ltd. This paper proposes a method to allow users to select target species for detection, generate an initial detection model by selecting a small piece of image sample and as the movie plays, continue training this detection model automatically. This method has noticeable detection results for several types of objects. The framework of this study is divided into two parts: the initial detection model and the online learning section. The detection model initialization phase use a sample size based on the proportion of users of the Haar-like features to generate a pool of features, which is used to train and select effective classifiers. Then, as the movie plays, the detecting model detects the new sample using the NN Classifier with positive and negative samples and the similarity model calculates new samples based on the fusion background model to calculate a new sample and detect the relative similarity to the target. From this relative similarity-based conservative classification of new samples, the conserved positive and negative samples classified by the video player are used for automatic online learning and training to continuously update the classifier. In this paper, the results of the test for different types of objects show the ability to detect the target by choosing a small number of samples and performing automatic online learning, effectively reducing the manpower needed to collect a large number of image samples and a large amount of time for training. The Experimental results also reveal good detection capability

A Dynamic Data Driven Application System for Vehicle Tracking

AbstractTracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle's movements by repeatedly identifying the vehicle under investigation from live image and video data, predicting probable future locations, and repositioning sensors or retargeting requests for information in order to reacquire the vehicle. An overview of the envisioned system is described that includes image processing algorithms to detect and recapture the vehicle from live image data, a computational framework to predict probable vehicle locations at future points in time, and a power aware data distribution management system to disseminate data and requests for information over ad hoc wireless communication networks. A testbed under development in the midtown area of Atlanta, Georgia in the United States is briefly described

Application of improved you only look once model in road traffic monitoring system

The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms

Deteksi Jenis Kendaraan di Jalan Menggunakan OpenCV

Jenis kendaraan yang melewati suatu ruas jalan dapat diketahui secara komputasi dengan mencocokkan fitur kendaran yang terdeteksi dengan fitur kendaraan standar masing-masing jeniskendaraan. Dengan mencapture frame image yang memuat kendaraan di jalan, OpenCV dapat mencocokkan fitur kendaraan tersebut dengan fitur kendaraan standar masing-masing jeniskendaraan, sehingga jenis kendaraan pada frame image dapat diketahui