Automatic multi-vehicle tracking using video cameras: An improved CAMShift approach

In the current Intelligent Transportation Systems (ITS) traffic monitoring and incident detection are usually supported with mostly traditional and relatively slow reactivity technologies. In this paper we propose a new service, namely THOR (Traffic monitoring Hybrid ORiented service), able to combine two different wireless technologies and to provide real time information about vehicular traffic monitoring and incident detection. THOR relies on LTE (Long Term Evolution) and Dedicated Short Range Communication based VANETs (Vehicular ad-hoc NETworks) in a hybrid approach, which is compliant with ITS standards. This hybrid networking approach can be deployed today and can be ready for tomorrow VANET technology. We test THOR by simulations in a scenario with vehicle flows synthesized from real measured vehicular traffic traces. We provide an LTE load analysis and an assessment of incident detection capabilities. Our results are promising in terms of reactivity, precision and network traffic load sustainability.

Intelligent Transportation System (ITS) is the emerging transportation which comprises of the advanced technologies such as wireless communications, video vehicle detection, distributed system architectures, human machine interface, sensing and actuating, to improve safety of the passengers, traffic congestion, fuel consumption and optimize other services of the transportation system such as real time traffic situation, electronic toll collection, automatic road enforcement and hot lanes. The current ITS systems lacks self-adaptive capability to take spontaneous decisions. The environment of the ITS system is very complex so optimal decisions cannot be taken but a good enough decision can be taken which fulfills the requirement of the system. To build an ITS system having the capability to adapt itself, this paper presents the self-adaptive requirements for the ITS. To elicit the requirements for ITS, we use scenarios based model and goal based model using the requirements Engineering processes (Elicitation, Elaboration, Specification, Validation) with MAPE-K. We use Knowledge Acquisition on Automated Specification (KAOS) approach to build goal model using Objectiver tool, and use RELAX syntax to elicit adaptive requirements for ITS.

This paper presents stereo vision-based vehicle detection approach on the road using a road feature and disparity histogram. It is not easy to detect only vehicles robustly on the road in various traffic situations, for example, a nonflat road or a multiple-obstacle situation. This paper focuses on the improvement of vehicle detection performance in various real traffic situations. The approach consists of three steps, namely obstacle localization, obstacle segmentation, and vehicle verification. First, we extract a road feature from v-disparity maps binarized using the most frequent values in each row and column, and adopt the extracted road feature as an obstacle criterion in column detection. However, many obstacles still coexist in each localized obstacle area. Thus, we divide the localized obstacle area into multiple obstacles using a disparity histogram and remerge the divided obstacles using four criteria parameters, namely the obstacle size, distance, and angle between the divided obstacles, and the difference of disparity values. Finally, we verify the vehicles using a depth map and gray image to improve the performance. We verify the performance of our proposed method by conducting experiments in various real traffic situations. The average recall rate of vehicle detection is 95.5%.

We propose an efficient algorithm for removing shadows of moving vehicles caused by non-uniform distributions of light reflections in the daytime. This paper presents a brand-new and complete structure in feature combination as well as analysis for orientating and labeling moving shadows so as to extract the defined objects in foregrounds more easily in each snapshot of the original files of videos which are acquired in the real traffic situations. Moreover, we make use of Gaussian Mixture Model (GMM) for background removal and detection of moving shadows in our tested images, and define two indices for characterizing non-shadowed regions where one indicates the characteristics of lines and the other index can be characterized by the information in gray scales of images which helps us to build a newly defined set of darkening ratios (modified darkening factors) based on Gaussian models. To prove the effectiveness of our moving shadow algorithm, we carry it out with a practical application of traffic flow detection in ITS (Intelligent Transportation System)—vehicle counting. Our algorithm shows the faster processing speed, 13.84 ms/frame, and can improve the accuracy rate in 4%~ 10% for our three tested videos in the experimental results of vehicle counting.

Intelligent Transportation System (ITS) is the emerging transportation which comprises of the advanced technologies such as wireless communications, video vehicle detection, distributed system architectures, human machine interface, sensing and actuating, to improve safety of the passengers, traffic congestion, fuel consumption and optimize other services of the transportation system such as real time traffic situation, electronic toll collection, automatic road enforcement and hot lanes. In order to build an ITS system having the capability to adapt itself, this paper presents the self -adaptive requirements for the ITS.

Traffic incident detection: Sensors and algorithms

Traffic systems are man-made systems in which the human component as a complex factor plays a significant part. [1] The aim of the research was to offer a method which is applicable for setting the real traffic situations at a signalized intersection against the simulated ones, using a traffic model with similar statistical features. The simplest technical apparatus has been used to monitor the real traffic situations. Only one video camera is needed for data collection and it is located in the observable area. Hence time-dependent parameters have been collected which are available for the analysis of the traffic situations. In the case of both traffic models the analysis is based on a behavior model of inhomogeneous driver-populations and an estimation method. The behavior model of inhomogeneous driver-populations handles the time-dependent parameters of driver behavior as the most important predictors, describes the movement and the behavior of the inhomogeneous driver-populations approaching a signalized intersection, passing the amber signal light. The estimation method developed using the model parameters opens the door to estimating the composition of drivers and measuring the capacity of an intersection on a more sensitive scale.

For significant characteristics such as high efficiency and environmental protection, electric vehicles (EVs) have become a new technology trend in the intelligent transportation system (ITS). Data like real time traffic situation and charge point occupation in ITS shows significant real-time characteristics. Distributed storage system, an effective technology to ensure reliable sharing of dynamic data, is first adopted to storage allocation for delay-sensitive data in this article. In order to improve the recovery probability of delay-sensitive data within its timeliness, we establish an access queuing delay model based on the characteristics of sensitive data. Then, we find the optimal storage allocation strategy across distributed storage nodes for data with different delay threshold in terms of the recovery probability. The analysis shows that for data with low real-time performance and delay sensitivity, the maximal symmetric allocation is more excellent. For real-time data with low delay threshold, the minimal allocation is better under the condition of limited storage budget.

Intelligent Transportation Systems (ITS) helps to improve the quality and quantity of many car traffic parameters. The use of the ITS is possible when the adequate measuring infrastructure is available. Video systems allow for its implementation with relatively low cost due to the possibility of simultaneous video recording of a few lanes of the road at a considerable distance from the camera. The process of tracking can be realized through different algorithms, the most attractive algorithms are Bayesian, because they use the a priori information derived from previous observations or known limitations. Use of this information is crucial for improving the quality of tracking especially for difficult observability conditions, which occur in the video systems under the influence of: smog, fog, rain, snow and poor lighting conditions.

Of the range of wireless communication technologies, wireless sensor networks (WSN) will be one of the most appropriate technologies for the Microgrid and Grid-enabled Vehicles in the Smartgrid. To ensure the security of WSN, the detection of attacks is more efficient than their prevention because of the lack of computing power. Malicious packet drops are the easiest means of attacking WSNs. Thus, the sensors used for constructing a WSN require a packet drop monitoring agent, such as Watchdog. However, Watchdog has a partial drop problem such that an attacker can manipulate the packet dropping rate below the minimum misbehaviour monitoring threshold. Furthermore, Watchdog does not consider real traffic situations, such as congestion and collision, and so it has no way of recognizing whether a packet drop is due to a real attack or network congestion. In this paper, we propose a malicious packet drop monitoring agent, which considers traffic conditions. We used the actual traffic volume on neighbouring nodes and the drop rate while monitoring a sending node for specific period. It is more effective in real network scenarios because unlike Watchdog it considers the actual traffic, which only uses the Pathrater. Moreover, our proposed method does not require authentication, packet encryption or detection packets. Thus, there is a lower likelihood of detection failure due to packet spoofing, Man-In-the Middle attacks or Wormhole attacks. To test the suitability of our proposed concept for a series of network scenarios, we divided the simulations into three types: one attack node, more than one attack nodes and no attack nodes. The results of the simulations meet our expectations.

SVM and KNN ensemble learning for traffic incident detection

Recent research in Intelligent Transportation System (ITS) focuses on Automatic Incident Detection (AID) techniques. Using advances in wireless networking and sensor technologies, modern vehicles have the ability to communicate with each other as well as with roadside infrastructure units (RSUs) in order to increase road safety. These new innovations in transportation technology provide traffic management system the ability to use data collected from the vehicles on the road to detect congestion and traffic incidents. Lately, many techniques were developed to alert drivers in advance about traffic incidents and to enable them to avoid congestion. In this paper, we discuss probabilistic collection of traffic data through vehicle-to-infrastructure (V2I) communications and present two novel techniques for automatic detection of traffic incidents in a highway scenario that are based on the use of distance and time for changing lanes, respectively vehicle speed changes over time. The proposed methods, which are illustrated with numerical results obtained from simulations, outperform alternative AID techniques through higher incident detection rates, about 25% shorter peak queue values and 20% faster dissipation of roadway congestion.

Vehicular ad hoc networks (VANETs) have emerged in the past decades as a significant type of networks, which consists of vehicles with sensors to communicate. For its ad-hoc nature, VANETs have great potential in a large number of applications, within which rear-end collision warning and traffic automatic incidents detection are two major applications. Because of the large number of injury and consequent economic loss, rear-end traffic collision has become an important issue and attracted a large number of attentions. In the past decades, there have been lots of efforts paid on this field. Existing work usually employed mathematical approaches or machine-learning approaches. In this study, we develop a collaborative rear-end collision warning algorithm (CORECWA), which is able to estimate and assess traffic risk in a collaborative and real-time way, and further notify drivers the warning message timely. Experiments results have shown that our algorithm outperforms the predominant method, HONDA algorithm. On the other hand, traffic incidents detection has been a critical problem in the past decades, due to the considerable economical cost and inestimable disgruntlement from numerous drivers. We present a support vector machines (SVM)-based approach for automatic incident detection (AID), in which the traffic data are collected by VANETs techniques. We process collected data and utilize traffic variables in the SVM model to confirm whether an incident occurs. Several experiments have been conducted to evaluate our approach’s performance, and the results show that our approach could outperform the other two approaches in most cases.

We present a method for estimating large camera motions, including combinations of large rotations and displacements. We know that translation estimates are improved as the displacements between two camera positions increase. However, there is little work on motion estimation methods targeted at large motions since it is difficult to find correspondences between two planar perspective images under large motions. We can overcome the correspondence problem with omnidirectional cameras, which obtain a hemisphere or cylinder projection of the environment. We first develop a new motion estimation method for smooth omnidirectional camera motions. Our method incrementally improves estimates from incomplete information provided by a single feature correspondence, using an Implicit Extended Kalman Filter (IEKF). This general motion estimation method produces stable estimates from smooth camera motions, but it is not directly applicable to estimating large motions. We adapt the motion estimation method to large motions by using a novel Recursive Rotation Factorization (RRF) that removes the image motions due to rotation. Simulation results show that the RRF large motion estimates are accurate and robust.

Traffic congestion has been causing many setbacks and challenges in the major developing and developed smart cities in India and all over the world. The typical traffic light sequence works on the specific switching of Red, Green and Yellow lights in a particular way with stipulated time irrespective of peak traffic hour and situation, this makes typical traffic light sequence method inefficient for present situation where density of traffic is exponentially increasing over the time. In this work, we propose and develop a design of Intelligent Traffic Light Control System to manage the road traffic according to the real time traffic situation. This work is intended to propose methods of dynamic time allocation for traffic lights according to traffic density and situation. In this paper, we acquired real time traffic density using image sensors and obstacle detector, where according to the traffic density, traffic controller dynamically generates “ON” and “OFF” time of traffic light sequence. The EDGE Spartan 6 FPGA (Field Programmable Gate Array) development board is programmed as controller to implement the proposed work and MATLAB programming in used to capture real time traffic image and generate dynamic time for the traffic light sequence to control the traffic situation in real time.