• Smart Media Journal
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• v.13 no.5
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• pp.9-18
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• 2024

Multi-Object trajectory modeling is a major challenge in MOT. CenterTrack tried to solve this problem with a Heatmap-based method that tracks the object center position. However, it showed limited performance when tracking objects with complex movements and nonlinearities. Considering the degradation factor of CenterTrack as the dynamic movement of pedestrians, we integrated the EKF into CenterTrack. To demonstrate the superiority of our proposed method, we applied the existing KF and UKF to CenterTrack and compared and evaluated it on various datasets. The experimental results confirmed that when EKF was integrated into CenterTrack, it achieved 73.7% MOTA, making it the most suitable filter for CenterTrack.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.636-644
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• 2015

This research aims to suggest methods to improve object tracking performance by combining CAM shift algorithm with 8-way search window, and reduce arithmetic operation by reducing the number of frame used for tracking. CAM shift has its adverse effect in tracking methods using signature color or having difficulty in tracking rapidly moving object. To resolve this, moving search window of CAM shift makes it possible to more accurately track high-speed moving object after finding object by conducting 8-way search by using information at a final successful timing point from a timing point missing tracking object. Moreover, hardware development led to increased unnecessary arithmetic operation by increasing the number of frame produced per second, which indicates efficiency can be enhanced by reducing the number of frame used in tracking to reduce unnecessary arithmetic operation.

• Journal of Platform Technology
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• v.11 no.4
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• pp.3-12
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• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.175-181
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• 1998

This paper describes realtime visual tracking system of moving object for three dimensional target using EKF(Extended Kalman Filter). We present a new realtime visual tracking using EKF algorithm and image prediction algorithm. We demonstrate the performance of these tracking algorithm through real experiment. The experimental results show the effectiveness of the EKF algorithm and image prediction algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.209-212
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• 2005

This paper describes detection and tracking of a moving object for unmanned visual surveillance. security systems. Using images obtained from camera it detects and tracks a moving object and displays bounding box enclosing the moving object. The algorithm for detection and tracking is tested using a personal computer, and then implemented on EMPOS II embedded system. Simulation results show that the tracking of a moving object based on embedded system is working well. However it needs to improve image acquisition time for real time implementation to apply security systems.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.52-57
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• 2004

In this paper, we propose a panorama-based object tracking scheme for wide-view surveillance systems that can detect and track moving objects with a pan-tilt camera. A dynamic mosaic of the background is progressively integrated in a single image using the camera motion information. For the camera motion estimation, we calculate affine motion parameters for each frame sequentially with respect to its previous frame. The camera motion is robustly estimated on the background by discriminating between background and foreground regions. The modified block-based motion estimation is used to separate the background region. Each moving object is segmented by image subtraction from the mosaic background. The proposed tracking system has demonstrated good performance for several test video sequences.

• International Journal of Control, Automation, and Systems
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• v.3 no.3
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• pp.444-452
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• 2005

A new scheme for a mobile robot to track and capture a moving object using camera images is proposed. The moving object is assumed to be a point-object and is projected onto an image plane to form a geometrical constraint equation that provides the position data of the object based on the kinematics of the active camera. Uncertainties in position estimation caused by the point-object assumption are compensated for using the Kalman filter. To generate the shortest time path to capture the moving object, the linear and angular velocities are estimated and utilized. In this paper, the experimental results of the tracking and capturing of a target object with the mobile robot are presented.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.174-177
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• 2009

In object tracking, the template matching methods have been developed and frequently used. It is fast enough, but not robust to an object with the variation of size and shape. In order to overcome the limitation of the template matching method, this paper proposes a template update technique. After finding an object position using the correlation-based adaptive predictive search, the proposed method selects blocks which contain object's boundary. It estimates the motion of boundary using block matching, and then updates template. We applied it to IR image sequences including an approaching object. From the experimental results, the proposed method showed successful performance to track object.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.52-58
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• 2002

This Paper describes the method of real time object tracking using variable searching window. Monitoring systems require real time object tracking in video, efficiencies depend on environment of monitoring target. To get a position of object using a difference between background image and input image, the system extracts contour and centroid of the object. This method track motion of object using variable searching window from size and position of object. The background imgaes and camera are limited as fixed environment. The test result of proposed method Is 17-23FPS, this shows more fast process speed than average(10-14FPS) of existing object tracking method.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.6
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• pp.562-570
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• 2012

This paper proposes a robust object tracking algorithm using object features and on-line learning based particle filter for mobile robots. Mobile robots with a side-view camera have problems as camera jitter, illumination change, object shape variation and occlusion in variety environments. In order to overcome these problems, color histogram and HOG descriptor are fused for efficient representation of an object. Particle filter is used for robust object tracking with on-line learning method IPCA in non-linear environment. The validity of the proposed algorithm is revealed via experiments with DBs acquired in variety environment. The experiments show that the accuracy performance of particle filter using combined color and shape information associated with online learning (92.4 %) is more robust than that of particle filter using only color information (71.1 %) or particle filter using shape and color information without on-line learning (90.3 %).