• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.49-57
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• 2016

In this paper, we propose the novel tracking method which uses the weighted discriminative correlation filter (DCF). We also propose the PSPR instead of conventional PSR as tracker performance evaluation method. The proposed tracking method uses multiple DCF to estimates the target position. In addition, our proposed method reflects more weights on the correlation response of the tracker which is expected to have more performance using PSPR. While existing multi-DCF-based tracker calculates the final correlation response by directly summing correlation responses from each tracker, the proposed method acquires the final correlation response by weighted combining of correlation responses from the selected trackers robust to given environment. Accordingly, the proposed method can provide high performance tracking in various and complex background compared to multi-DCF based tracker. Through a series of tracking experiments for various video data, the presented method showed better performance than a single feature-based tracker and also than a multi-DCF based tracker.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.7
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• pp.587-594
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• 2005

In this paper, a robust correlation-based video tracking is proposed to track a moving object in correlated image sequences. A correlation-based video tracking algorithm seeks to align the incoming target image with the reference target block image, but has critical problems, so called a false-peak problem and a drift phenomenon (correlator walk-off. The false-peak problem is generally caused by highly correlated background pixels with similar intensity of a moving target and the drift phenomenon occurs when tracking errors accumulate from frame to frame because of the nature of the correlation process. At first, the false-peaks problem for the ordinary correlation-based video tracking is investigated using a simple mathematical analysis. And, we will suggest a robust selective-attention correlation measure with a gradient preprocessor combined by a drift removal compensator to overcome the walk-off problem. The drift compensator adaptively controls the template block size according to the target size of interest. The robustness of the proposed method for practical application is demonstrated by simulating two real-image sequences.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.3
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• pp.107-114
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• 2015

Binary Offset Carrier (BOC) signal planned for future Global Navigation Satellite System (GNSS) provided better positioning accuracy and smaller multipath error than GPS C/A signal. However, due to the multiple side peaks in the auto-correlation function (ACF) of the BOC modulated signals, a receiver may false lock onto one of the side peaks in the tracking mode. This false lock would then result in a fatal tracking error. In this paper, we propose an unambiguous tracking method for composite BOC (CBOC) signals to mitigate this problem. It aims to reduce the side peaks of the ACF of CBOC modulated signals. It is based on the combination of traditional CBOC correlation function (CF) and reference CF of unmodulated pseudo- random noise code (PRN code). First, we present that cross-correlation function (CCF) with unmodulated PRN code is close to the secondary peaks of the traditional CBOC. Then, we obtain an unambiguous correlation function by subtracting traditional CBOC ACF from these CFs. Finally, the tracking performance for the CBOC signals is examined, and it is shown that the proposed method has better performance than the traditional unambiguous tracking method in additive white Gaussian noise (AWGN) channel.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.55-61
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• 2017

In this paper, we propose a novel tracking method using target separation and detection that are based on discriminative correlation filter (DCF), which is studied a lot recently. 'Retainability' is one of the most important factor of tracking. There are some factors making retainability of tracking worse. Especially, fast movement and occlusion of a target frequently occur in image data, and when it happens, it would make target lost. As a result, the tracking cannot be retained. For maintaining a robust tracking, in this paper, separation of a target is used so that normal tracking is maintained even though some part of a target is occluded. The detection algorithm is executed and find new location of the target when the target gets out of tracking range due to occlusion of whole part of a target or fast movement speed of a target. A variety of experiments with various image data sets are conducted. The algorithm proposed in this paper showed better performance than other conventional algorithms when fast movement and occlusion of a target occur.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.552-555
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• 2012

The main goal of this paper is to develop a robust visual tracking algorithm with particle filtering. Visual Tracking with particle filter technique is not easy task due to cluttered environment, illumination changes. To deal with these problems, we develop an efficient observation model for target tracking with particle filter. We develop a robust phase correlation combined with motion information based observation model for particle filter framework. Phase correlation provides straight-forward estimation of rigid translational motion between two images, which is based on the well-known Fourier shift property. Phase correlation has the advantage that it is not affected by any intensity or contrast differences between two images. On the other hand, motion cue is also very well known technique and widely used due to its simplicity. Therefore, we apply the phase correlation integrated with motion information in particle filter framework for robust tracking. In experimental results, we show that tracking with multiple cues based model provides more reliable performance than single cue.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1433-1440
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• 2015

In this paper, we propose an unambiguous correlation function to improve tracking performance for binary offset carrier (BOC) signals. Specifically, we divide a BOC sub-carrier into multiple rectangular pulses, and analyze that the BOC autocorrelation function is made up of the sum of several partial correlation functions. Then, we obtain two sub-correlation functions by combining two partial correlation functions and propose a novel unambiguous correlation function with no side-peak which can be regulated its width based on the combination of the sub-correlation functions and partial correlation functions. From numerical results, it is confirmed that the proposed correlation function provides a tracking performance improvement over the conventional correlation functions in terms of the tracking error standard deviation.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.230-238
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• 1998

When size and shape of moving object have been changed, a correlator often accumulates walk-off error. A success of correlation-based tracking largely depends on choosing suitable window size and position and thus transferring the proper reference image to the next frame. For this, we propose the Adaptive Window Algorithm with Four-Direction Sizing Factors (AWA-FSF) for fast adjusting a reference region to enhance reliability of correlation-based image tracking in complex cluttered environments. Since the AWA-FSF is capable of adjusting a reference image size more rapidly and properly, we can minimize the influence of complex background and clutter. In addition, we can finely tune the center point of the reference image repeatedly after main tracking process. Thus we have increased stability and reliability of correlation-based image tracking. We tested performance of the AWA-FSF using 45 real image sequences made of over 3400 images and had the satisfied results for most of them.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.5
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• pp.409-415
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• 2013

In this paper, we propose a correlation function using newly designed local signals for cosine-phased binary offset carrier (BOC) signal tracking. First, we divide a sub-carrier pulse over one pseudo random noise code period into multiple rectangular pulses, and subsequently, design novel local signals. Then, we obtain a correlation function with no side-peak based on a combination of correlations between the newly generated local signals and received cosine-phased BOC signal. From numerical results, it is confirmed that the proposed correlation function provides a tracking performance improvement over the conventional correlation functions in terms of the tracking error standard deviation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.512-519
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• 2013

In this paper, we propose a novel unambiguous correlation function for composite binary offset carrier (CBOC) signal tracking. First, we observe that a sub-carrier of CBOC signal is seen as a sum of four partial sub-carriers, and generate four partial-correlations composing the CBOC autocorrelation. Then, we obtain an unambiguous correlation function with a sharp main-peak by re-combining the partial correlations. From numerical results, we confirm that the proposed unambiguous correlation function offers a better tracking performance than the conventional correlation functions in terms of the tracking error standard deviation and multipath error envelope.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2094-2112
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• 2019

Focusing on the issue that the conventional Kernel Correlation Filter (KCF) algorithm has poor performance in handling scale change and obscured objects, this paper proposes an anti-occlusion and scale adaptive tracking algorithm in the basis of KCF. The average Peak-to Correlation Energy and the peak value of correlation filtering response are used as the confidence indexes to determine whether the target is obscured. In the case of non-occlusion, we modify the searching scheme of the KCF. Instead of searching for a target with a fixed sample size, we search for the target area with multiple scales and then resize it into the sample size to compare with the learnt model. The scale factor with the maximum filter response is the best target scaling and is updated as the optimal scale for the following tracking. Once occlusion is detected, the model updating and scale updating are stopped. Experiments have been conducted on the OTB benchmark video sequences for compassion with other state-of-the-art tracking methods. The results demonstrate the proposed method can effectively improve the tracking success rate and the accuracy in the cases of scale change and occlusion, and meanwhile ensure a real-time performance.