A Cluster Based Feasible Time Interval for Tracking Lost or Stolen Vehicle

In particle filter based vehicle tracking, the second order autoregression model and the fixed particle propagation radius are often used for particles sampling. This would produce certain errors and cause the particles to deviate gradually from the vehicle's true location in tracking. To resolve this problem, a modified state transition equation is built. In this equation, the vehicle's current location is estimated using the motion vector of its center block and the particle propagation radius is updated using kalman filter. Both improvements make the state transition equation more accurate. The experiment results show that the proposed method can decrease the particles' deviation and track vehicles more accurately than the particle filter using the second order autoregression model and the fixed particle propagation radius.

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.

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user’s defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

At present, most vehicle tracking algorithms are tested in an ideal environment (definition video, without attitude change, without occlusion). However, due to the vehicle variability (scale change, attitude change) and complexity (noise, occlusion) of the surrounding environment in real-life scenarios, there are still many difficulties for the vehicle target accurately tracking. In order to adapt to blur, occlusion, fast moving, illumination change and other complex situation in real scene, the multi-scale Retinex image enhancement algorithm and the mutual information occlusion processing algorithm are introduced into structured output SVM tracking algorithm. Experiments show that the tracking accuracy and robustness of the method are improved significantly on the premise of tracking task performance in the above case.

Vehicular Ad-hoc Networks (VANETs) is the most eminent field nowadays in Intelligent Transportation System. Applications included emergency alerts, positioning, and tracking of vehicles. Vehicle Localization in municipal areas is a major issue for protection applications. Many solutions have been provided including Global Positioning Systems (GPS) but these applications do not provide accuracy. Hence, a novel approach has been proposed here known as Received Signal Strength (RSS) Based Localization which aims to find accurate location of a target vehicle. It provides communication with Road Side Units (RSUs) by receiving signal within its range, and finds the average RSS. After the RSS has been found it is aided to the RSS Based Localization algorithm which finds accurate location of the vehicle. The main factor of proposed algorithm is its high signal to noise ratio which is obtained from the closest RSU. After the location of the vehicle is found, its Cramer Rao Lower Bound is analyzed. All the simulations performed shows that our suggested RSS based Localization are better than others traditional least squares and weighted least squares techniques.

In recent days, vehicles have become equipped with electric system that assist and help drivers driving safe in highway. Lane Departure Warning and Lane Keeping Assistant System are one of those safe system. Sudden and inattentive lateral motion of vehicle due to drivers' fatigue, illness, inattention, and drowsiness are major causes of accidents in highway. In order to prevent and minimize any possible accidents due to lateral motion of vehicle, active steering control system is designed and simulated in this study. Activation and warning areas are defined for the control system with respect to the location of vehicle and parameters for vehicle dynamics. The current location of vehicle is obtained by Differential Global Positioning System (DGPS) and it can detect DGPS-based lanes then determine whether activates or not.

In highway environment, many safety-related applications demand localization. Currently, localization is usually achieved by on-vehicle GPS. However, it requires that each car has a GPS device, which increases vehicle's cost and restricts the scope of some applications to some extent. The road side unit (RSU) is a fix infrastructure to collect information, enhance road safety and indicate navigations, and will be furnished on the highway as well as urban environment in the near future. At the same time, the RSU can also be used to provide initial position to the driving-by vehicles because of its fixed position and high capacity of wireless transmission. In addition, the vehicle's velocity follows normal distribution and the trajectory is fixed by one-dimensional and straight-line highway path. Therefore, the dead reckoning can be used to compute the vehicle's current location after obtaining its initial position. In this paper, we propose an RSU-assisted localization method using dead reckoning and vehicle-to-RSU communications. Our method can be used in non-GPS highway traffic environment with high accuracy and feasibility.

Rapid urbanization and growing environmental concerns have increased the demand for sustainable and intelligent transportation systems within institutional environments. University campuses experience continuous internal mobility involving students, faculty members, and service vehicles, which often results in energy consumption and environmental pollution when conventional fuel-based transportation systems operate within campus infrastructure. Electric vehicles offer a clean and energy-efficient transportation alternative capable of supporting short-distance mobility with zero tailpipe emissions and reduced noise levels. Integration of Internet of Things technologies with electric vehicle systems enables the development of intelligent monitoring frameworks that support real-time supervision, operational efficiency, and centralized management of campus transportation fleets. IoT-enabled monitoring systems utilize interconnected sensors, communication modules, and cloud-based platforms to collect and analyze operational data related to battery status, vehicle location, energy consumption, and system performance. Continuous monitoring of electric vehicle fleets allows transportation administrators to visualize operational data through digital dashboards, improving vehicle tracking, fleet coordination, and maintenance planning. Integration of vehicle tracking systems with campus mobility platforms enhances route optimization, transportation scheduling, and efficient vehicle allocation across institutional environments. Energy optimization and battery monitoring mechanisms further support sustainable electric vehicle operations by enabling intelligent charging management and energy consumption analysis. Data-driven insights generated from IoT platforms strengthen predictive maintenance strategies and improve long-term reliability of electric vehicle fleets operating within campus transportation systems. Development of IoT-based electric vehicle monitoring frameworks therefore contributes significantly to pollution-free campus mobility by improving operational efficiency, reducing carbon emissions, and supporting sustainable smart campus transportation infrastructure.

Based on the Global Positioning System (GPS), VELOC (vehicle location) is designed to be a vehicle information system for fleet management adaptable to various user groups, e.g., haulage and delivery companies, and service enterprises with vehicle fleets. The needs of these groups vary with respect to position accuracy, position update rate, and type of communication. The authors describe the requirements, specifications, and performance of VELOC. Special emphasis is placed on some substantial features of the VELOC center, namely the integration of DGPS, a comfortable user interface, and handling of vehicle positions on digital maps. >

Aiming at the defects and shortages of traditional moving vehicles detection algorithms, by the analysis and comparison of the existing detection algorithms, we propose an algorithm that combined with frames with symmetric difference and background difference to detect moving vehicle in this paper. First, two different difference images by using frames with symmetric difference and background difference are gained respectively and two binary images can be gained by the appropriate threshold, then the contour of moving vehicles can be extracted by applying OR operation in the two binary images. Finally, the precise moving vehicles will be gained by mathematic morphological methods. In this paper we use Harris operator, Feature Points such as edges and corners are extracted, followed by block-matching to track the Feature Points in successive viedo frames. Many vehicles can be tracked at the same time automatically since the information is obtained from video sequences. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2613

To Track the position of the vehicle by means of GPS device and automatically sending the data to the remote server webpage and display the current position of the device in the Google map to the authorities. In institution or in ATM cash filling vehicle, most of the time we need to monitor position or path vehicle whether it’s moving in correct direction or not. This system used the ARM7 controller. The real time vehicle tracking and monitoring system tracks the vehicle and displays the current location of the vehicle in the remote server using Google maps. It has the ability to communicate over the remote areas where user needs the current location of vehicle. It can provide Tele-monitoring system for inter-cities transportation vehicles such as taxis and buses. This system is integrated with GPS and GSM to provide features like Location information and Real time tracking using SMS.

Traffic density on roads directly affects the arrival time of emergency services. Effective monitoring of traffic flow and shortly time of traffic control will help facilitate the arrival time of emergency services. In this paper, some traffic monitoring measurements systems are included. Vehicle tracking algorithms are one of the systems that can provide traffic data, and the system can be configured to project visual images for analysis. Congested transportation is a key issue in Hong Kong. Being able to collect traffic density data for analysis can provide effective and substantive data for sustainable development. To provide unobstructed traffic, thereby reducing lane density and reducing obstruction to emergency services. There are video processing methods to understand traffic conditions. These systems can only capture different traffic conditions to evaluate traffic details. But it cannot control traffic conditions.

A smart traffic analysis system could be used to reduce congestion, prevent accidents, as well as to control traffic flow. Such a system would need to make use of many technologies, such as computer networking, communication, image processing, object detection, and tracking. In this paper, we introduce an efficient vehicle tracking algorithm which could be used to help create a smart city traffic control system. Our algorithm is targeted to solve the multi-object tracking (MOT) problem. Our method follows a track-by-detection paradigm, i.e., it relies on vehicle detection results to decide whether a detected vehicle in sequential frames belong to the same track. Our vehicle tracker extends the intersection over union (IOU) tracker and improves upon it by fusing historical tracking information, taking into consideration the balance between tracking efficiency and effectiveness. We demonstrate the effectiveness and efficiency of our approach using the UA-DETRAC benchmark dataset. Our proposed method runs at an average speed of 1,264 frames per second. We conclude that our tracker could be useful for applications running in a real-time environment.

The moving object or vehicle location prediction based on their spatial and temporal information is an important task in many applications. Different methods were utilized for performing the vehicle movement detection and prediction process. In such works, there is a lack of analysis in predicting the vehicles location in current as well as in future. Moreover, such methods compute the vehicles movement by finding the topological relationships among trajectories and locations, whereas the representative GPS points are determined by the 30 m circular window. Due to this process, the performance of the method is degraded because such 30 m circular window is selected by calculating the error range in the given input image and such error range may vary from image to image. To reduce the drawback presented in the existing method, in this study a heuristic moving vehicle location prediction algorithm is proposed. The proposed heuristic algorithm mainly comprises two techniques namely, optimization GA algorithm and FFBNN. In this proposed technique, initially the vehicles frequent paths are collected by monitoring all the vehicles movement in a specific period. Among the frequent paths, the vehicles optimal paths are computed by the GA algorithm. The selected optimal paths for each vehicle are utilized to train the FFBNN. The well trained FFBNN is then utilized to find the vehicle movement from the current location. By combining the proposed heuristic algorithm with GA and FFBNN, the vehicles location is predicted efficiently. The implementation result shows the effectiveness of the proposed heuristic algorithm in predicting the vehicles future location from the current location. The performance of the heuristic algorithm is evaluated by comparing the result with the RBF classifier. The comparison result shows our proposed technique acquires an accurate vehicle location prediction ratio than the RBF prediction ratio, in terms of accuracy.

This paper proposed the novel system which can track the car in the local area based on the RFID and Zigbee technology. This system uses the circuit board developed by ourselves. It combined with the PLC, the wireless communication and the database technology. It has designed to display the dynamical positioning of cars in the map of the area through the website. The tags from RFID system carry the automotive information. Then the information recognized by the reader has been transmitted to the upper computer by the Zigbee module. The website on the upper computer query the vehicle's location from database after mapping the fixed position information sent by the reader into the view of the schematic graph. Because the database has stored the vehicle's location. All the obtained records are used to draw the full path of the car and display the current location of the car. This provides the basis for the scientific navigation. The website of the system has been finished in the Visual Studio platform and the Mysql database technology. And it used the B/S architecture and achieved the vehicle tracking. This system also finishes the dynamic light system and improves the management efficiency for vehicles and can be applied widely.