• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2642-2664
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• 2024

In the process of modern traffic management, information technology has become an important part of intelligent traffic governance. Real-time monitoring can accurately and effectively track and record vehicles, which is of great significance to modern urban traffic management. Existing tracking algorithms are affected by the environment, viewpoint, etc., and often have problems such as false detection, imprecise anchor boxes, and ID switch. Based on the YOLOv5 algorithm, we improve the loss function, propose a new feature extraction module to obtain the receptive field at different scales, and do adaptive fusion with the SGE attention mechanism, so that it can effectively suppress the noise information during feature extraction. The trained model improves the mAP value by 5.7% on the public dataset UA-DETRAC without increasing the amount of calculations. Meanwhile, for vehicle feature recognition, we adaptively adjust the network structure of the DeepSort tracking algorithm. Finally, we tested the tracking algorithm on the public dataset and in a realistic scenario. The results show that the improved algorithm has an increase in the values of MOTA and MT etc., which generally improves the reliability of vehicle tracking.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.435-442
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• 2015

In this paper, we propose a tracking and recognition of pedestrian/vehicle for intelligent multi-visual surveillance system. The intelligent multi-visual surveillance system consists of several fixed cameras and one calibrated PTZ camera, which automatically tracks and recognizes the detected moving objects. The fixed wide-angle cameras are used to monitor large open areas, but the moving objects on the images are too small to view in detail. But, the PTZ camera is capable of increasing the monitoring area and enhancing the image quality by tracking and zooming in on a target. The proposed system is able to determine whether the detected moving objects are pedestrian/vehicle or not using the SVM. In order to reduce the tracking error, an improved camera calibration algorithm between the fixed cameras and the PTZ camera is proposed. Various experimental results show the effectiveness of the proposed system.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.3
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• pp.363-370
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• 2016

This paper proposes a Ku-band multi-mode coupler and its monopulse tracking system, which can be applied to a unmaned aircraft vehicle(UAV) platform. In general, the carrier-to-noise(C/N) level of the beacon signal from a Ku-band commercial satellite is relatively weak compared to that of a military satellite because the Ku-band satellite has been designed for commercial services. Therefore, this paper proposes a coupler and its multi-mode monopulse tracking system satisfying the tracking accuracy under a low C/N environment and analyzes the tracking accuracy. After that, we perform a real satellite tracking test and compare the accuracy of the test with the analysis result before validating the performance of the architecture of the proposed satellite tracking system.

• Journal of Mechanical Science and Technology
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• v.19 no.9
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• pp.1742-1752
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• 2005

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.816-819
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• 2011

The image recognition system using CCTV camera has been introduced to minimize not only loss of life and property but also traffic jam in the tunnel. In this paper, multi-object detection algorithm is proposed to track multi vehicles. The proposed algorithm is to detect multi cars based on Adaboost and to track multi vehicles to use template matching. As results of simulations, it is shown that proposed algorithm is useful for tracking multi vehicles.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.700-702
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• 2016

The Doppler Radar that mounted on Electro Optical Tracking System has been operated to measure range and velocity during the initial mission of space launch vehicle at Naro space center. In this paper, we mentioned configuration of MFCW(Multi frequency Continuous Wave) and FMCW(Frequency Modulation Continuous Wave) Doppler Radar on Electro Optical Tracking System and described method of range measurement.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.47-56
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• 2015

In the multi-sensor system, sensor registration errors such as a sensor bias must be corrected so that the individual sensor data are expressed in a common reference frame. If registration process is not properly executed, large tracking errors or formation of multiple track on the same target can be occured. Especially for launch vehicle tracking system, each multiple observation lies on the same reference frame and then fused trajectory can be the best track for slaving data. Hence, this paper describes an on-line bias estimation/correction and asynchronous sensor fusion for launch vehicle tracking. The bias estimation architecture is designed based on pseudo bias measurement which derived from error observation between GPS and radar measurements. Then, asynchronous sensor fusion is adapted to enhance tracking performance.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.764-781
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• 2013

In this paper, we propose a highly precise vehicle detection method with low false alarm using billboard sweep stereo matching and multi-stage hypothesis generation. First, we capture stereo images from cameras established in front of the vehicle and obtain the disparity map in which the regions of ground plane or background are removed using billboard sweep stereo matching algorithm. And then, we perform the vehicle detection and tracking on the labeled disparity map. The vehicle detection and tracking consists of three steps. In the learning step, the SVM(support vector machine) classifier is obtained using the features extracted from the gabor filter. The second step is the vehicle detection which performs the sobel edge detection in the image of the left camera and extracts candidates of the vehicle using edge image and billboard sweep stereo disparity map. The final step is the vehicle tracking using template matching in the next frame. Removal process of the tracking regions improves the system performance in the candidate region of the vehicle on the succeeding frames.

• Journal of Advanced Navigation Technology
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• v.3 no.2
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• pp.156-166
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• 1999

In multi-vehicle tracking systems Kalman filter is generally used for tracking vehicles. Despite well known advantages of Kalman filter that presents optimality with constraints, it is difficult to track several vehicles in real time simultaneously due to a large number of computations. In this paper, we propose a multi-vehicle tracking system with an adaptive predictor that employs recursive least square algorithm which can be easily implemented for real time application on a transversal filter. The performance of the proposed tracking system is compared to one with Kalman filter using a synthetic sequential image generated by computer graphics and real sequential image taken at intersections. Simulation results show that the proposed tracking system can be applied to track vehicles in real sequential image at the rate of 30 frame/sec on a PC environments without any special hardwares.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.186-190
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• 2015

Intelligent vehicle plans motion and navigate itself based on the surrounding environment perception. Hence, the precise environment recognition is an essential part of self-driving vehicle. There exist many vulnerable road users (e.g. vehicle, pedestrians) on vehicular driving environment, the vehicle must percept all the dynamic obstacles accurately for safety. In this paper, we propose an multiple vehicle tracking algorithm using microwave radar. Our proposed system includes various special features. First, exceptional radar measurement model for vehicle, concentrated on the corner, is described by mixture density network (MDN), and applied to particle filter weighting. Also, to conquer the curse of dimensionality of particle filter and estimate the time-varying number of multi-target states, reversible jump markov chain monte carlo (RJMCMC) is used to sampling step of the proposed algorithm. The robustness of the proposed algorithm is demonstrated through several computer simulations.