Vehicle Tracking Method Research Articles

Vehicle Positioning and Tracking Algorithm in the Visual Positioning System in Ship Cabin

A large cargo ship is widely used for vehicle transporting, and it is necessary to position vehicles in the cabin. For the characteristic of large space, low height of ship cabin and large vehicle, building a multi-camera positioning system for vehicles is the optimal one. In this paper, a high accuracy vehicle positioning algorithm is proposed to locate multiple moving vehicles in the positioning system. Firstly, the conversion relationship between the pixel coordinate and the world coordinate is calculated. Zhang’s calibration method is adopted to correct the lens distortion, the images are stitched to get the complete cabin image and the height of vehicle body is calibrated. Secondly, a method for vehicle positioning and tracking is proposed. Background subtraction method is adopted to detect the preliminary vehicle position, and Spatio-Temporal Context (STC) algorithm is adopted to track the mark on the vehicle body. The experimental results show that in 2.56m*0.62m space of the simulated ship cabin, for three vehicle models with size of 0.45m*0.14m*0.15m, the horizontal positioning accuracy can reach 2cm each, less than 1%. With the proposed algorithm, multiple moving vehicles positioning with high accuracy and high precision in ship cabin can be reached.

Turn Signal Detection During Nighttime by CNN Detector and Perceptual Hashing Tracking

Detecting vehicle turn signals at night is critical for both assistant driving systems and autonomous driving systems. In this paper, we propose a novel method that consists of detection and tracking modules to achieve a high level of robustness. For nighttime vehicle detection, a Nakagami-image-based method is used to locate the regions containing vehicle lights. At the same time, a set of vehicle object proposals is generated using a region proposal network based on convolutional neural network (CNN) feature maps. Then, the light regions and proposals are combined to generate the regions of interest (ROIs) for the further detection. Vehicle candidates are extracted from the ROIs using a softmax classifier with CNN-based features. For the tracking module, we propose a perceptional hashing algorithm to track these vehicle candidates. During the tracking, turn signals are detected by analyzing the continuous intensity variation of the vehicle box sequences. Experimental results for typical sequences show that the proposed method can robustly detect and track a vehicle in front with over 95% accuracy and recognize the turning signals in night scenes with a detection rate of over 90%. The vehicle detection method improves the miss rate of state-of-the-art systems by more than 20%. In addition, the proposed vehicle tracking method outperforms other state-of-the-art systems.

Logistics vehicle tracking method based on intelligent vision

ABSTRACTThe traditional logistics vehicle tracking method lacks the function of active identification and switch tracking. Therefore, in the presence of interference, there are some problems such as interference, hard recognition, handoff delay and tracking loss for the tracking of no difference vehicle. Therefore, a logistics vehicle tracking method based on intelligent vision is proposed. Firstly, the visual vehicle is segmented by visual vehicle, and then image feature of logistics vehicle is obtained, the visual intelligent tracking method based on region matching and Kalman filter is used to change the logistics vehicle without difference. In the handover process, the Kalman filter is used to predict the position of the vehicle, and the compensation switching delay is compensated. According to consistency of the vehicle running state and the corresponding lane, no-difference switching logistics vehicle tracking is realized. The experimental results show that the algorithm is not demanded for the initial background. The algorithm can automatically generate the current frame background regardless of the presence of the moving vehicle in the initial background. The logistics efficiency of the method is higher than 90%; error tracking rate is less than 1%.

A novel approach in real-time vehicle detection and tracking using Raspberry Pi

Real-time vehicle detection, tracking and counting of vehicles is of great interest for researchers and is a need of the society in general for comfortable, smooth and safe movements of vehicles in cities. We propose a video image processing algorithm which detects, tracks and finds the number of vehicles on a road. We convert RGB video frame to HSV color domain, which helps in differentiating the colors of the vehicles more absolutely. The noise is removed from each frame. Detection of the vehicles is purely carried on color features of the vehicles. Vehicle tracking is done using Kalman filter with the data association. The number of vehicles running in a video or in a particular lane is determined. We propose a novel idea to detect, track and count the vehicles on a road and it has been implemented on Raspberry Pi 3 using OpenCV and C++. We have compared the results of the proposed method with that of rear-view vehicle detection and tracking method (Bin et al., 2014) and morphological operation method (Zezhong et al., 2013), and found that the proposed algorithm is more effective in terms of accuracy of vehicle detection and cost.

An Enhanced Viola-Jones Vehicle Detection Method From Unmanned Aerial Vehicles Imagery

This research develops an advanced vehicle detection method, which improves the original Viola-Jones (V-J) object detection scheme for better vehicle detections from low-altitude unmanned aerial vehicle (UAV) imagery. The original V-J method is sensitive to objects’ in-plane rotation, and therefore has difficulties in detecting vehicles with unknown orientations in UAV images. To address this issue, this research proposes a road orientation adjustment method, which rotates each UAV image once so that the roads and on-road vehicles on rotated images will be aligned with the horizontal direction and the V-J vehicle detector. Then, the original V-J can be directly applied to achieve better efficiency and accuracy. The enhanced V-J method is further applied for vehicle tracking. Testing results show that both vehicle detection and tracking methods are competitive compared with other existing methods. Future research will focus on expanding the current methods to detect other transport modes, such as buses, trucks, motorcycles, bicycles, and pedestrians.

Efficient Real-Time Train Operation Algorithms With Uncertain Passenger Demands

The majority of existing studies in subway train operations focus on timetable optimization and vehicle tracking methods, which may be infeasible with disturbances in actual operations. To deal with uncertain passenger demands and realize real-time train operations (RTOs) satisfying multiobjectives, including overspeed protection, punctuality, riding comfort, and energy consumption, this paper proposes two RTO algorithms via expert knowledge and an online learning approach. The first RTO algorithm is developed by a knowledge-based system to ensure the multiple objectives with a constant timetable. Then, by considering uncertain passenger demand at each station and random running time errors, we convert the train operation problem into a Markov decision process with nondeterministic state transition probabilities in which the aim is to minimize the reward for both the total time delay and energy consumption in a subway line. After designing policy, reward, and transition probability, we develop an integrated train operation (ITO) algorithm based on Q-learning to realize RTOs with online adjusting the timetable. Finally, we present some numerical examples to test the proposed algorithms with real detected data in the Yizhuang Line of Beijing Subway. The results indicate that, taking the multiple objectives into account, the RTO algorithm outperforms both manual driving and automatic train operations. In addition, the ITO algorithm is capable of dealing with uncertain disturbances, keeping the total time delay within 2 s and reducing the energy consumption.

A real-time multiple vehicle tracking method for traffic congestion identification

Traffic congestion is a severe problem in many modern cities around the world. Real-time and accurate traffic congestion identification can provide the advanced traffic management systems with a reliable basis to take measurements. The most used data sources for traffic congestion are loop detector, GPS data, and video surveillance. Video based traffic monitoring systems have gained much attention due to their enormous advantages, such as low cost, flexibility to redesign the system and providing a rich information source for human understanding. In general, most existing video based systems for monitoring road traffic rely on stationary cameras and multiple vehicle tracking method. However, most commonly used multiple vehicle tracking methods are lack of effective track initiation schemes. Based on the motion of the vehicle usually obeys constant velocity model, a novel vehicle recognition method is proposed. The state of recognized vehicle is sent to the GM-PHD filter as birth target. In this way, we relieve the insensitive of GM-PHD filter for new entering vehicle. Combining with the advanced vehicle detection and data association techniques, this multiple vehicle tracking method is used to identify traffic congestion. It can be implemented in real-time with high accuracy and robustness. The advantages of our proposed method are validated on four real traffic data.

Video-based tracking of vehicles using multiple time-spatial images

An innovative idea of vehicle tracking for video-based intelligent traffic management system is known to bring significant socioeconomic impact. A successful vehicle tracking method is always in demand to monitor different traffic parameters such as the average speed, strange movements, and congestion of vehicles or even to detect accidents automatically on highways or freeways. The challenges of traditional video-based vehicle tracking methods include the initialization of tracking to tackle an unknown number of targets and the reduction of the drift sensitivity of targets from true positions mainly caused by the variations in lighting condition, occlusions and camera position. To address these challenges, this paper presents a novel vehicle tracking method for a traffic management system that introduces the multiple time-spatial images (MTSIs)-based detection in the stochastic filter-based tracking. The MTSI-based tracking employs the concept of multiple numbers of key vehicular frames (KVFs) for each of the vehicular-objects in the traffic. These KVFs provide highly accurate positional information of the vehicles due to the fact that the shape and texture of the vehicles are comparable on the same scale and do not depend on the speed of the traffic. The spatial correspondence of a vehicle in successive KVFs is then incorporated as a low-complexity data association technique to alleviate the common problem of drifting in the stochastic filter-based method and thereby increasing the accuracy in tracking trajectory. Comprehensive experimentations are carried out using two publicly available video databases (EBVT and GRAM-RTM) that have traffics of varying environments to evaluate the vehicle tracking performance of the proposed method as compared to the existing methods. Experimental results demonstrate that the introduction of MTSIs not only automates the initialization of tracking, but also significantly increases the accuracy of the tracking trajectories of the vehicles on roads evaluated both in the presence and absence of ground truths.

Direction-Based Modified Particle Filter for Vehicle Tracking

This research proposes a modified particle filter to increase the accuracy of vehicle tracking in a noisy and occluded medium. In our proposed method for vehicle tracking, the direction angle of a target vehicle is calculated. The angular difference between the motion direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted depending on their angular distance to the motion direction. Those particles moving in a direction similar to that of the target vehicle are assigned larger weights; this, in turn, increases their probability in a given likelihood function (part of the process of estimation of a target's state parameters). The proposed method is compared against a condensation algorithm. Our results show that the proposed method improves the stability of a particle filter tracker and decreases the particle consumption.

An improved multiple model particle filtering approach for manoeuvring target tracking using airborne GMTI with geographic information

This paper proposes a ground vehicle tracking method using an airborne ground moving target indicator radar where the surrounding geographic information is considered to determine vehicle's movement type as well as constrain its positions. Multiple state models corresponding to different movement modes are applied to represent the vehicle's behaviour in different terrain conditions. Based on geographic conditions and multiple state models, a constrained variable structure multiple model particle filter algorithm is proposed. Compared with the traditional multiple model particle filtering schemes, the proposed algorithm utilises a particle swarm optimisation technique which generates more effective particles and generated particles are constrained into the feasible geographic region. Numerical simulation results in a realistic environment show that the proposed method achieves better tracking performance compared with current state-of-the-art ones for manoeuvring vehicle tracking.

A Novel Method for Effective Vehicle Detection and Tracking Eliminating Occlusion

Intelligent Transportation System (ITS) plays a significant role in constructing a modern transportation to safeguard the lives of people along the busy roads. In ITS, Visual traffic surveillance using computer vision techniques makes it more sophisticated to achieve better traffic control. This paper proposes a new traffic surveillance system to detect and track the vehicles effectively using the video captured by vertical positioned camera. Vehicle detection is based on motion detection, kernel density estimation in a pixel-based technique is used for motion detection. The 3D pose estimation using an Evolutionary computing framework is used to handle all kinds of occlusion. Then the 3D model based tracking is achieved by using Unscented Kalman Filter. The proposed model performs better in foreground object detection, tracking and vehicle speed estimation.

Multiple Vehicle Detection and Tracking in Highway Traffic Surveillance Video Based on SIFT Feature Matching

This paper presents a complete method for vehicle detection and tracking in a fixed setting based on computer vision. Vehicle detection is performed based on Scale Invariant Feature Transform (SIFT) feature matching. With SIFT feature detection and matching, the geometrical relations between the two images is estimated. Then, the previous image is aligned with the current image so that moving vehicles can be detected by analyzing the difference image of the two aligned images. Vehicle tracking is also performed based on SIFT feature matching. For the decreasing of time consumption and maintaining higher tracking accuracy, the detected candidate vehicle in the current image is matched with the vehicle sample in the tracking sample set, which contains all of the detected vehicles in previous images. Most remarkably, the management of vehicle entries and exits is realized based on SIFT feature matching with an efficient update mechanism of the tracking sample set. This entire method is proposed for highway traffic environment where there are no non-automotive vehicles or pedestrians, as these would interfere with the results.

Learning for an aesthetic model for estimating the traffic state in the traffic video

With the increasing number of vehicles running on the urban roads, the traffic jam becomes much more serious. Properly estimating the traffic jam level from traffic videos is essential for the department of transportation management and drivers. Currently, for estimating the traffic state on videos, most solutions are built on evaluating traffic flow by counting the running vehicles per time unit or detecting their moving speed. However, the main challenge of these solutions is on the vehicle tracking method, in which the vehicles are necessary to be effectively and integrally segmented from the scenes. The solutions should tradeoff the accuracy of the estimation results and the efficiency of the method. In this paper, we propose a learning-based aesthetic model to estimate the traffic state on videos. The model uses multiple video-based perceptual features about traffic state to train the random forest classifier with the labeled data, and estimates traffic state by data classification. The evaluation experiments are conducted on a testing image set, and the results show that the traffic state estimation accuracy of the proposed model is higher than 98% and the efficiency performance is achieved in real-time.

Vehicle Tracking Based on Fusion of Magnetometer and Accelerometer Sensor Measurements With Particle Filtering

In this paper, we propose a method for vehicle tracking on roadways using measurements of magnetometers and accelerometers. The measurements are used to build a low-cost, low-complexity vehicle tracking sensor platform for highway traffic monitoring. First, the problem is formulated by introducing the process model for the motion of the vehicle on the road and two measurement models: one for each of the sensors. Second, it is shown how the measurements of the sensors can be fused using particle filtering. The standard sampling importance resampling (SIR) particle filter is extended for processing of multirate sensor measurements and models that employ unknown static parameters. The latter are treated by Rao–Blackwellization. The performance of the method is demonstrated by computer simulations. It is found that it is feasible to fuse the two sensors for vehicle tracking and that the proposed multirate particle filter performs better than particle filters that process only measurements of one of the sensors. The main contribution of this paper is the novel approach of fusing the measurements of road-mounted magnetometers and accelerometers for vehicle tracking and traffic monitoring.

Vehicle Tracking Based on Fuzzy C-Means Clustering and Grey Model

To detect the moving vehicles correctly, we proposed a vehicle tracking method based on background model, fuzzy C-Means clustering and Grey model. In this paper, the background image is updated adaptively to accommodate vehicle detection in variable outer environment. The Grey model is employed to predict the center and number of C-Means clustering, solving the dependency of fuzzy C-Means upon clustering center of initial frame. The result of clustering provides updated data of Grey model for the position of target in the next frame. The experimental results show that, the method proposed improves both of the speed and accuracy of C-Means clustering, and promote the robustness and real-time of vehicle tracking system.

Preceding vehicle detection and tracking adaptive to illumination variation in night traffic scenes based on relevance analysis.

Preceding vehicle detection and tracking at nighttime are challenging problems due to the disturbance of other extraneous illuminant sources coexisting with the vehicle lights. To improve the detection accuracy and robustness of vehicle detection, a novel method for vehicle detection and tracking at nighttime is proposed in this paper. The characteristics of taillights in the gray level are applied to determine the lower boundary of the threshold for taillights segmentation, and the optimal threshold for taillight segmentation is calculated using the OTSU algorithm between the lower boundary and the highest grayscale of the region of interest. The candidate taillight pairs are extracted based on the similarity between left and right taillights, and the non-vehicle taillight pairs are removed based on the relevance analysis of vehicle location between frames. To reduce the false negative rate of vehicle detection, a vehicle tracking method based on taillights estimation is applied. The taillight spot candidate is sought in the region predicted by Kalman filtering, and the disturbed taillight is estimated based on the symmetry and location of the other taillight of the same vehicle. Vehicle tracking is completed after estimating its location according to the two taillight spots. The results of experiments on a vehicle platform indicate that the proposed method could detect vehicles quickly, correctly and robustly in the actual traffic environments with illumination variation.

Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance

Traffic surveillance is an important topic in intelligent transportation systems. Robust vehicle detection and tracking is one challenging problem for complex urban traffic surveillance. This paper proposes a rear-view vehicle detection and tracking method based on multiple vehicle salient parts using a stationary camera. We show that spatial modeling of these vehicle parts is crucial for overall performance. First, the vehicle is treated as an object composed of multiple salient parts, including the license plate and rear lamps. These parts are localized using their distinctive color, texture, and region feature. Furthermore, the detected parts are treated as graph nodes to construct a probabilistic graph using a Markov random field model. After that, the marginal posterior of each part is inferred using loopy belief propagation to get final vehicle detection. Finally, the vehicles' trajectories are estimated using a Kalman filter, and a tracking-based detection technique is realized. Experiments in practical urban scenarios are carried out under various weather conditions. It can be shown that our method adapts to partial occlusion and various lighting conditions. Experiments also show that our method can achieve real-time performance.

Detection and Tracking of Vehicle Target Based on Super-Resolution Reconstruction and Variable Template Matching

Vehicle Target Detection and Tracking Method Based on Image Super-Resolution Reconstruction and Variable Template Matching is Put Forward. Firstly, a Nonlinear Iterative Algorithm is Applied to Reconstruct a Super-Resolution Image from Low Resolution Image Sequence; then, the Image is Standardized and the Movement Areas are Determined; Finally, the Variable Template Matching Method is Used to Detect and Track the Vehicle Targets in Movement Areas. from the Characteristics of Algorithm and the Experiment Results, we can see that the Proposed Algorithm Improves the Matching Accuracy of Target Tracking and Better Solves the Limitation of Missed Detection for Traditional Methods. the Reason of the Good Performance of the Proposed Algorithm Relies in High Quality Images Acquired by Super-Resolution Reconstruction from Low Resolution Image Sequence and the Application of Variable Template Matching Method.

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

Video cameras play an important role in achieving the potentials promised by Intelligent Transportation Systems. In particular, video tracking systems have been widely applied in traffic flow data collection and incident detection by tracking and analyzing vehicle trajectories through video image processing. Focusing on current vehicle tracking methods that are limited to complex traffic scenes such as target non-uniqueness and color distraction in real traffic situations, this paper proposes an improved Continuously Adaptive Mean Shift (CAMShift) method for automatic multi-vehicle tracking using video cameras. The proposed method firstly uses a background subtraction method for automatically detecting, selecting, and initializing targets, i.e., vehicles. Moreover, a motion estimation method is proposed for estimating vehicle motion states and predicting the new locations of the vehicles. Finally, both the color and edge feature distributions of the vehicles are extracted and a mixed model is established for searching the matching vehicles in the next image frame. The proposed method was comparatively evaluated with the traditional CAMShift method using the same video sequences captured from real traffic situations. Evaluation results showed that the proposed method is efficient for accurately tracking vehicle movements, and is promising for practical applications.

Architecture Design of the Vehicle Tracking System Based on RFID

Vehicle tracking plays more important roles in modern transportation and logistics operation. This paper deals with a new approach to track vehicles based on RFID (Radio Frequency Identification) technology. The vehicle tracking system is designed overallly supported by Axiomatic Design theory.The basic steps of vehicle tracking based on RFID are developed and a six-layered architecture for the vehicle tracking system integrating databases, RFID tags, RFID readers, data centers, networks and user interface is presented, where the positions of vehicles are attained directly by compared the RFID readers collecting vehicle RFID tags their position information in the database. It is shown that this vehicle tracking method uses RFID more fully, the architecture with clear levels, adapts to the future technical and practical requirements, and can help fuse more complicated applications like intelligent charging and cargo tracking. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2606