• Journal of Broadcast Engineering
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• v.25 no.2
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• pp.192-199
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• 2020

Recently, the utilization of the object tracking algorithm based on the deep learning model is increasing. A system for tracking multiple objects in an image is typically composed of a chain form of an object detection algorithm and an object tracking algorithm. However, chain-type systems composed of several modules require a high performance computing environment and have limitations in their application to actual applications. In this paper, we propose a method that enables real-time operation in low-performance computing environment by adjusting the computational process of object detection module in the object detection-tracking chain type system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.163-169
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• 2008

In this paper, we propose a real-time people tracking system with multiple CCD cameras for security inside the building. The camera is mounted from the ceiling of the laboratory so that the image data of the passing people are fully overlapped. The implemented system recognizes people movement along various directions. To track people even when their images are partially overlapped, the proposed system estimates and tracks a bounding box enclosing each person in the tracking region. The approximated convex hull of each individual in the tracking area is obtained to provide more accurate tracking information. To achieve this goal, we propose a method for 3D walking human tracking based on the IMPRESARIO framework incorporating cascaded classifiers into hypothesis evaluation. The efficiency of adaptive selection of cascaded classifiers have been also presented. We have shown the improvement of reliability for likelihood calculation by using cascaded classifiers. Experimental results show that the proposed method can smoothly and effectively detect and track walking humans through environments such as dense forests.

• Journal of the Korean Society of Safety
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• v.23 no.6
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• pp.164-170
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• 2008

This research was verified detection way of intercept vehicles and performance evaluation after system installation using image detector as detection way of ground installation. By image recognition algorithm was on the trace of moving orbit of violation vehicles for detection way of intercept vehicles. When moving orbit is located special site, utilized geometric image calibration and DC-notch filter. These are cognitive system of license plate by making signal. Then, Bright Evidence Detection and Dark Evidence Detection were applied to after mixing. It is applied to way of Backward tracking for detection way of intercept vehicles. After the field evaluation of developed system, it should be analyzed the more high than recognition rate of minimum standards 80%. It should rise in the estimation of the site applicability is highly from now.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.427-433
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• 2010

Although tracking methods are efficient and popular for vessel segmentation, they require a seed to initiate an instance of tracking. In this paper, a new method to detect new seeds for tracking of arterial segments from CT angiography (CTA) and to construct a vascular structure is proposed. The proposed algorithm is based on shape analysis of connected components in a volume of interest around a vessel segment which was already extracted by tracking. The eigenvalues of the covariance matrix are used as the shape features for detection. The experimental results on actual clinical data showed that the results totally revealed the arterial tree not hindered by bone or veins. In visual comparison to a method which combines registration and subtraction of both pre-contrast and post-contrast CT volumes, the proposed method produced comparable results to the reference method and were confirmed of its feasibility for clinical use of reducing the cost and burden of patients.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.12
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• pp.265-274
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• 2012

In this paper, we propose a method for preceding vehicle detection and tracking with motion estimation by radar-vision sensor fusion. The motion estimation proposed results in not only correction of inaccurate lateral position error observed on a radar target, but also adaptive detection and tracking of a preceding vehicle by compensating the changes in the geometric relation between the ego-vehicle and the ground due to the driving. Furthermore, the feature-based motion estimation employed to lessen computational burden reduces the number of deployment of the vehicle validation procedure. Experimental results prove that the correction by the proposed motion estimation improves the performance of the vehicle detection and makes the tracking accurate with high temporal consistency under various road conditions.

• Journal of IKEEE
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• v.13 no.2
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• pp.232-240
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• 2009

This paper presents a robust vehicle detection and tracking algorithm for supervision of illegal parking. The proposed algorithm is composed of four parts. First, a vehicle detection algorithm is proposed using the improved codebook object detection algorithm to segment moving vehicles from the input sequence. Second, a preprocessing technique using the geometric characteristics of vehicles is employed to exclude non-vehicle objects. Then, the detected vehicles are tracked by an object tracker which incorporates histogram tracking method with Kalman filter. To make the tracking results more accurate, histogram tracking results are used as measurement data for Kalman filter. Finally, Real Stop Counter (RSC) is introduced for trustworthy and accurate performance of the stopped vehicle detection. Experimental results show that the proposed algorithm can track multiple vehicles simultaneously and detect stopped vehicles successfully in the complicated street environment.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.263-269
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• 2010

This paper describes real-time detection and tracking of moving objects for unmanned visual surveillance. Using images obtained from the fixed camera it detects moving objects within the image and tracks them with displaying rectangle boxes enclosing the objects. Tracking method is implemented on an embedded system which consists of TI DSK645.5 kit and the FPGA board connected on the DSP kit. The DSP kit processes image processing algorithms for detection and tracking of moving objects. The FPGA board designed for image acquisition and display reads the image line-by-line and sends the image data to DSP processor, and also sends the processed data to VGA monitor by DMA data transfer. Experimental results show that the tracking of moving objects is working satisfactorily. The tracking speed is 30 frames/sec with 320x240 image resolution.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.3
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• pp.33-40
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• 2006

In towed array passive sonar system, sonar operators cannot detect and track the all targets simultaneously in the omni-directional area by just Operator Initiated Tracking(OIT). In this paper, omni-directional automatic target detection and tracking algorithm is described and optimize the parameters through ocean data to overcome the drawbacks of OITs. The algorithm is verified through sea trials with submarines.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.405-408
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• 2011

In this paper, we propose a robot remote control system based on mouth tracking. The main idea behind the work is to help disabled people who cannot operate a joystick or keyboard to control a robot with their hands. The mouth detection method in this paper is mainly based on the Adaboost feature detection approach. By using the proposed new Haar-like features for detecting the corner of mouth, the speed and accuracy of detection are improved. Combined with the Kalman filter, a continuous and accurate mouth tracking has been achieved. Meanwhile, the gripping commands of the robot manipulator were also achieved by the recognition of the user.s mouth shape, such as 'pout mouth' or 'grin mouth'. To assess the validity of the method, a mouth detection experiment and a robot cargo transport experiment were applied. The result indicated that the system can realize a quick and accurate mouse tracking; and the operation of the robot worked successfully in moving and bringing back items.