• Journal of Advanced Navigation Technology
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• v.22 no.1
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• pp.20-26
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• 2018

In this paper, we propose a moving object detection and tracking algorithm based on multi-frame feature point tracking information to reduce false positives. However, there are problems of detection error and tracking speed in existing studies. In order to compensate for this, we first calculate the corner feature points and the optical flow of multiple frames for camera movement compensation and object tracking. Next, the tracking error of the optical flow is reduced by the multi-frame forward-backward tracking, and the traced feature points are divided into the background and the moving object candidate based on homography and RANSAC algorithm for camera movement compensation. Among the transformed corner feature points, the outlier points removed by the RANSAC are clustered and the outlier cluster of a certain size is classified as the moving object candidate. Objects classified as moving object candidates are tracked according to label tracking based data association analysis. In this paper, we prove that the proposed algorithm improves both precision and recall compared with existing algorithms by using quadrotor image - based detection and tracking performance experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.338-347
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• 2005

In this paper, a decimation-in-time search direction algorithm for displacement prediction of moving object is proposed. The initialization of the proposed algorithm for moving direction prediction is performed by detecting moving objects at sequential frames and by obtaining a moving angle and a moving distance. A moving direction of the moving object at current frame is obtained by applying the decimation-in-time search direction mask. The decimation-in-tine search direction mask is that the moving object is detected by thinning out frames among the sequential frames, and the moving direction of the moving object is predicted by the search mask which is decided by obtaining the moving angle of the moving object in the 8 directions. to examine the propriety of the proposed algorithm, velocities of a driving car are measured and tracked, and to evaluate the efficiency, the proposed algorithm is compared to the full search algorithm. The evaluated results show that the number of displacement search times is reduced up to 91.8$\%$ on the average in the proposed algorithm, and the processing time of the tracking is 32.1ms on the average.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.39-50
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• 2017

Moving object tracking techniques can be considered as one of the most essential technique in the video understanding of which the importance is much more emphasized recently. However, irregularity of light condition in the video, variations in shape and size of object, camera motion, and occlusion make it difficult to tracking moving object in the video. Swarm based methods are developed to improve the performance of Kalman filter and particle filter which are known as the most representative conventional methods, but these methods also need to consider dynamic property of moving object. This paper proposes adaptive parameter control method which can dynamically change weight value among parameters in particle swarm optimization. The proposed method classifies each particle to 3 groups, and assigns different weight values to improve object tracking performance. Experimental results show that our scheme shows considerable improvement of performance in tracking objects which have nonlinear movements such as occlusion or unexpected movement.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.2
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• pp.78-87
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• 2012

In this paper, we address the tracking problem caused by camera motion and rolling shutter effects associated with CMOS sensors in consumer handheld cameras, such as mobile cameras, digital cameras, and digital camcorders. A modified particle filtering method is proposed for simultaneously tracking objects and compensating for the effects of camera motion. The proposed method uses an elastic registration algorithm (ER) that considers the global affine motion as well as the brightness and contrast between images, assuming that camera motion results in an affine transform of the image between two successive frames. By assuming that the camera motion is modeled globally by an affine transform, only the global affine model instead of the local model was considered. Only the brightness parameter was used in intensity variation. The contrast parameters used in the original ER algorithm were ignored because the change in illumination is small enough between temporally adjacent frames. The proposed particle filtering consists of the following four steps: (i) prediction step, (ii) compensating prediction state error based on camera motion estimation, (iii) update step and (iv) re-sampling step. A larger number of particles are needed when camera motion generates a prediction state error of an object at the prediction step. The proposed method robustly tracks the object of interest by compensating for the prediction state error using the affine motion model estimated from ER. Experimental results show that the proposed method outperforms the conventional particle filter, and can track moving objects robustly in consumer handheld imaging devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1166-1191
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• 2011

This paper presents an adaptive active tracking system with sector based scanning for a single PTZ camera. Dividing sectors on an image reduces the search space to shorten selection time so that the system can cover many targets. Upon the selection of a target, the system estimates the target trajectory to predict the zooming location with a finite amount of time for camera movement. Advanced estimation techniques using probabilistic reason suffer from the unknown object dynamics and the inaccurate estimation compromises the zooming level to prevent tracking failure. The proposed system uses the simple piecewise estimation with a few frames to cope with fast moving objects and/or slow camera movements. The target is tracked in multiple steps and the zooming time for each step is determined by maximizing the zooming level within the expected variation of object velocity and detection. The number of zooming steps is adaptively determined according to target speed. In addition, the iterative estimation of a zooming location with camera movement time compensates for the target prediction error due to the difference between speeds of a target and a camera. The effectiveness of the proposed method is validated by simulations and real time experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.4
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• pp.298-306
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• 2013

Recent years have witnessed a growing interest in the fields of video surveillance and mobile object tracking. This paper proposes a mobile object tracking algorithm. First, several parameters such as object window, object area, and expansion-contraction (E-C) parameter are defined. Then, a modified E-C algorithm for multiple-object tracking is presented. The proposed algorithm tracks moving objects by expansion and contraction of the object window. In addition, it includes methods for updating the background image and avoiding occlusion of the target image. The validity of the proposed algorithm is verified experimentally. For example, the first scenario traces the path of two people walking in opposite directions in a hallway, whereas the second one is conducted to track three people in a group of four walkers.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.43-49
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• 2023

Object tracking is being studied with various techniques such as Kalman filter and Luenberger tracker. Even in situations, such as the one in which the system model is not well specified, to which existing signal processing techniques are not successfully applicable, it is possible to design artificial neural networks to track objects. In this paper, we propose an artificial neural network, which we call 'maximum-likelihood weighted-average neural network', to continuously track unpredictably moving objects. This neural network does not directly estimate the locations of an object but obtains location estimates by making weighted average combining various results of maximum likelihood tracking with different data lengths. We compare the performance of the proposed system with those of Kalman filter and maximum likelihood object trackers and show that the proposed scheme exhibits excellent performance well adapting the change of object moving characteristics.

• Journal of Broadcast Engineering
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• v.14 no.4
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• pp.419-427
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• 2009

We present a novel head tracking system for stereoscopic displays that ensures the viewer has a high degree of movement. The tracker is capable of segmenting the viewer from background objects using their relative distance. A depth camera using TOF(Time-Of-Flight) is used to generate a key signal for eye tracking application. A method of the moving parallax barrier is also introduced to supplement a disadvantage of the fixed parallax barrier that provides observation at the specific locations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.713-728
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• 2021

This paper proposes an augmented reality technique to generate acrobatic scenes from hitting motion videos. After a user shoots a motion that mimics hitting an object with hands or feet, their pose is analyzed using motion tracking with deep learning to track hand or foot movement while hitting the object. Hitting position and time are then extracted to generate the object's moving trajectory using physics optimization and synchronized with the video. The proposed method can create videos for hitting objects with feet, e.g. soccer ball lifting; fists, e.g. tap ball, etc. and is suitable for augmented reality applications to include virtual objects.