Road Vehicle Detection Research Articles

A Novel Approach for On-road Vehicle Detection and Tracking

On the basis of a necessary development of the road safety, vision-based vehicle detection techniques have gained an important amount of attention. This work presents a novel vehicle detection and tracking approach, and structured based on a vehicle detection process starting from, images or video data acquired from sensors installed on board of the vehicle, to vehicle detection and tracking. The features of the vehicle are extracted by the proposed GIST image processing algorithm, and recognized by the state-of-art Support Vectors Machine classifier. The tracking process was performed based on edge features matching approach. The Kalman filter was used to correct the measurements. Extensive experiments were carried out on real image data validate that it is promising to employ the proposed approach for on road vehicle detection and tracking.

Monocular based road vehicle detection with feature fusion and cascaded Adaboost algorithm

To decrease vehicle crash, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on DM642 DSP micro-controller is built. Then, a two-step vehicle detection algorithm is proposed. In the first step, a fast vehicle edge and symmetry fusion algorithm is obtained and a low threshold is set so that all possible vehicles will selected and also maintain a high false detection rate. In the second step, a classifier using Haar and HoG fusion feature based Adaboost classifier is trained to classify the possible vehicles and eliminate the false detected vehicles from the candidate vehicles generated in the first step. Experimental results demonstrate that this classifier maintains very high detection rate and low false detection rate in different road, weather and lighting conditions.

Detection and Classification of Vehicles by Measurement of Road-Pavement Vibration and by Means of Supervised Machine Learning

Road vehicle detection and, to a lesser extent, classification have received considerable attention, in particular for the purpose of traffic monitoring by transportation authorities. A multitude of sensors and systems have been developed to assist people in traffic monitoring. Camera-based systems have enjoyed wide adoption over the last decade, partially substituting for more traditional techniques. Methods based on road-pavement vibration are not as common as camera-based systems. However, vibration sensors may be of interest when sensors must be out of sight and insensitive to environmental conditions, such as fog. We present and discuss our work on detection and classification of vehicles by measurement of road-pavement vibration and by means of supervised machine learning. We describe the entire processing chain from sensor data acquisition to vehicle classification and discuss our results for the task of vehicle detection and the task of vehicle classification separately. Using data for a single vibration sensor, our results show a performance ranging between 94% and near 100% for the detection task (1340 samples) and between 43% and 86% for the classification task (experiment specific, between 454 and 1243 samples).

Dual Roadside Seismic Sensor for Moving Road Vehicle Detection and Characterization

This paper presents a method for using a dual roadside seismic sensor to detect moving vehicles on roadway by installing them on a road shoulder. Seismic signals are split into fixed time intervals in recording. In each interval, the time delay of arrival (TDOA) is estimated using a generalized cross-correlation approach with phase transform (GCC-PHAT). Various kinds of vehicle characterization information, including vehicle speed, axle spacing, detection of both vehicle axles and moving direction, can also be extracted from the collected seismic signals as demonstrated in this paper. The error of both vehicle speed and axle spacing detected by this approach has been shown to be less than 20% through the field tests conducted on an urban street in Seattle. Compared to most existing sensors, this new design of dual seismic sensor is cost effective, easy to install, and effective in gathering information for various traffic management applications.

Vehicle detection in driving simulation using extreme learning machine

Automatically driving based on computer vision has attracted more and more attentions from both research and industrial fields. It has two main challenges, high road and vehicle detection accuracy and real-time performance. To study the two problems, we developed a driving simulation platform in a virtual scene. In this paper, as the first step of final solution, the Extreme Learning Machine (ELM) has been used to detect the virtual roads and vehicles. The Support Vector Machine (SVM) and Back Propagation (BP) network have been used as benchmark. Our experimental results show that the ELM has the fastest performance on road segmentation and vehicle detection with the similar accuracy compared with other techniques.

Implementation of Vehicle Detection System Using WSN

Vehicle Detection System plays a basic role in the field of intelligent transportation, and is the cornerstone of constructing modern intelligent transportation system. This paper presents a new vehicle detection algorithm using WSN that called the adaptive state machine. The algorithm can adaptively update the threshold and baseline; use the state machine to achieve the aim of the accurate and efficient vehicle detection. It can be used for the detection of road traffic flow, and can be used in large parking vehicle guidance system. On the road, we have deployed 76 Sensor Nodes to evaluate the performance. We observe the accurate of the road vehicle detection rate of vehicle detection system is nearly 98%.

Prediction of Speed by Rain Intensity using Road Weather Information System and Vehicle Detection System data

Intelligent transportation systems allow us to have valuable opportunities for collecting reliable wide-area coverage traffic and weather data. Significant efforts have been made in many countries to apply these data. This study identifies the critical points for classifying rain intensity by analyzing the relationship between rainfall and the amount of speed reduction. Then, traffic prediction performance by rain intensity level is evaluated using relative errors. The results show that critical points are 0.4mm/5min and 0.8mm/5min for classifying rain intensity (slight, moderate, and heavy rain). The best prediction performance is observable when previous five-block speed data is used as inputs under normal weather conditions. On the other hand, previous two or three-block speed data is used as inputs under rainy weather conditions. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

3 Abstract. One of the most important methods to solve traffic congestion is to detect the incident state of a roadway. This paper describes the development of a method for road traffic monitoring aimed at the acquisition and analysis of remote sensing imagery. We propose a strategy for road extraction, vehicle detection and incident detection from remote sensing imagery using techniques based on neural networks, Radon transform for angle detection and traffic-flow measurements. Traffic-bottleneck detection is another method that is proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection method had a detection rate of 87.5%.

Highway Traffic Incident Detection using High-Resolution Aerial Remote Sensing Imagery

Problem statement: As vehicle population increases, Intelligent Transportation Systems (ITS) become more significant and mandatory in today’s overpopulated world. Vital problems in transportation such as mobility and safety of transportation are considered more, especially in metropolitans and highways. The main road traffic monitoring aims are: the acquisition and analysis of traffic figures, such as number of vehicles, incident detection and automatic driver warning systems are developed mainly for localization and safety purposes. Approach: The objective of this investigation was to propose a strategy for road extraction and incident detection using aerial images. Real time extraction and localization of roadways in an satellite image is an emerging research field which can applied to vision-based traffic controlling and unmanned air vehicles navigation. Results: The results of the proposed incident detection algorithm show that it has good detection performance, the maximum angle of vehicles applied for incidet detection is 45° and the performance for learning system in order to vehicle detection is 86%. This performance achived in testing algorithm on 45 highway aerial images. Conclusion: In order to consider with the high complexity of this kind of imagery, we integrate knowledge about roadways using formulated scale-dependent models. The intensity images are used for the extraction of road from satellite images. Threshold techniques, neural network and Radon transform are used for the road extraction, vehicle detection and incident detection. Results indicated that in most aerial images the incident can be detect by applying the angle algorithm.

Road environment modeling using robust perspective analysis and recursive Bayesian segmentation

Recently, vision-based advanced driver-assistance systems (ADAS) have received a new increased interest to enhance driving safety. In particular, due to its high performance–cost ratio, mono-camera systems are arising as the main focus of this field of work. In this paper we present a novel on-board road modeling and vehicle detection system, which is a part of the result of the European I-WAY project. The system relies on a robust estimation of the perspective of the scene, which adapts to the dynamics of the vehicle and generates a stabilized rectified image of the road plane. This rectified plane is used by a recursive Bayesian classifier, which classifies pixels as belonging to different classes corresponding to the elements of interest of the scenario. This stage works as an intermediate layer that isolates subsequent modules since it absorbs the inherent variability of the scene. The system has been tested on-road, in different scenarios, including varied illumination and adverse weather conditions, and the results have been proved to be remarkable even for such complex scenarios.

A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection

This paper presents an algorithm to detect vehicles on roads and to measure inter-vehicle distance. Hypothesis generation and verification are the bases of the algorithm. The hypothesis generation is composed of 1) transformation of a pair of perspective stereo images into a pair of virtual stereo top-view images; 2) construction of polar accumulation functions (PAFs) for the stereo top-view images; and 3) stereo matching of PAFs by dynamic programming. The verification is comprised of 1) temporal matching of PAFs by dynamic programming and 2) verifying the potential positions of objects. Through multiple experiments in various environments, the proposed algorithm has been proven to be effective in detecting vehicles on roads and for measuring inter-vehicle distance.

A semi-automatic framework for highway extraction and vehicle detection based on a geometric deformable model

Road extraction and vehicle detection are two of the most important steps of traffic flow analysis from multi-frame aerial photographs. The traditional way of deriving traffic flow trajectories relies on manual vehicle counting from a sequence of aerial photographs. It is tedious and time-consuming work. To improve this process, this research presents a new semi-automatic framework for highway extraction and vehicle detection from aerial photographs. The basis of the new framework is a geometric deformable model. This model refers to the minimization of an objective function that connects the optimization problem with the propagation of regular curves. Utilizing implicit representation of two-dimensional curve, the implementation of this model is capable of dealing with topological changes during curve deformation process and the output is independent of the position of the initial curves. A seed point propagation framework is designed and implemented. This framework incorporates highway extraction, tracking, and linking into one procedure. Manually selected seed points can be automatically propagated throughout a whole highway network. During the process, road center points are also extracted, which introduces a search direction for solving possible blocking problems. This new framework has been successfully applied to highway network extraction and vehicle detection from a large orthophoto mosaic. In this research, vehicles on the extracted highway network were detected with an 83% success rate.