• 한국멀티미디어학회논문지
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• 제23권10호
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• pp.1236-1249
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• 2020

In a most recent object tracking research work, applying Convolutional Neural Network and Recurrent Neural Network-based strategies become relevant for resolving the noticeable challenges in it, like, occlusion, motion, object, and camera viewpoint variations, changing several targets, lighting variations. In this paper, the LSTM Network-based Tracking association method has proposed where the technique capable of real-time multi-object tracking by creating one of the useful LSTM networks that associated with tracking, which supports the long term tracking along with solving challenges. The LSTM network is a different neural network defined in Keras as a sequence of layers, where the Sequential classes would be a container for these layers. This purposing network structure builds with the integration of tracking association on Keras neural-network library. The tracking process has been associated with the LSTM Network feature learning output and obtained outstanding real-time detection and tracking performance. In this work, the main focus was learning trackable objects locations, appearance, and motion details, then predicting the feature location of objects on boxes according to their initial position. The performance of the joint object tracking system has shown that the LSTM network is more powerful and capable of working on a real-time multi-object tracking process.

• 한국정보통신학회논문지
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• 제11권4호
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• pp.786-792
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• 2007

비디오 감시 시스템에서 물체의 추적은 매우 중요하다. 본 논문에서는 외부 환경에서 움직이는 물체를 추적하는 방법을 제안한다. 움직이는 물체를 추적하기 위하여 먼저 가중치 차 영상을 구하여 움직이는 물체를 추출한 후 다시 닫힘 연산을 사용하여 잡음을 제거한다. 그리고 추출된 다양한 특징 정보로 매칭하여 움직이는 물체를 추적한다. 제안된 추적 방법은 가중치 차 영상을 사용하여 움직이는 물체를 추적하기에 정지된 물체가 갑자기 움직이거나 갑자기 멈출 때도 정확히 추적할 수 있다. 본 논문에서 제안한 추적 시스템은 공간위치, 형상과 명암도 특징을 종합하기에 움직이는 물체를 보다 더 효과적으로 추적할 수 있다.

• 대한임베디드공학회논문지
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• 제9권1호
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• pp.53-59
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• 2014

In multiple object tracking, accurate detection for each of objects that appear sequentially and effective tracking in complicated cases that they are overlapped with each other are very important. In this paper, we propose a multiple object tracking system that has a concrete detection and tracking characteristics by using multi-lateral histogram and SIFT feature extraction algorithm. Especially, by limiting the matching area to object's inside and by utilizing the location informations in the keypoint matching process of SIFT algorithm, we advanced the tracking performance for multiple objects. Based on the experimental results, we found that the proposed tracking system has a robust tracking operation in the complicated environments that multiple objects are frequently overlapped in various of directions.

• 정보보호학회논문지
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• 제21권6호
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• pp.141-152
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• 2011

다양한 감시 환경에서의 보안의 중요성이 대두됨에 따라 여러 대의 카메라로 움직이는 물체를 연속적으로 추적하는 시스템에 대한 연구가 활발히 진행되고 있다. 본 논문은 물체를 연속적으로 추적하기 위해 비겹침 다중 카메라 기반의 영삼감시시스템을 제안한다. 제안된 다중 IP 카메라 기반 객체추적 기술은 장치 간 hand-off 기술 및 프로토콜을 바탕으로 객체추적 모듈과 추적관리 모듈로 구성된다. 객체추적 모듈은 IP 카메라에서 실행되며 객체추적 정보 생성, 객체추적 정보 공유, 객체추적 정보를 이용한 객체 검색 및 모듈 내 설정 기능을 제공하고, 추적관리 모듈은 영상관제 서버에서 실행되며 객체추적 정보 실시간 수신, 객체추적 정보 검색, IP 카메라 컨트롤 기능을 제공한다. 본 논문에서 제안한 객체추적 기술은 다양한 감시 환경과 기술 방법에 의존하지 않는 범용적 프레임워크를 제안한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.947-948
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• 2008

Recently, many efforts have been made for research and application of object tracking system. However, introduced object tracking algorithms have limitations to adopt a realtime object tracking system with multi camera. In this paper, we present a novel background generation and target object recognition algorithm for realtime object tracking system with multi camera and implemented it.

• 대한원격탐사학회지
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• 제40권1호
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1307-1317
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• 2018

To deal with the problems of occlusion, pose variations and illumination changes in the object tracking system, a regression model weighted multi-templates mean-shift (MS) algorithm is proposed in this paper. Target templates and occlusion templates are extracted to compose a multi-templates set. Then, the MS algorithm is applied to the multi-templates set for obtaining the candidate areas. Moreover, a regression model is trained to estimate the Bhattacharyya coefficients between the templates and candidate areas. Finally, the geometric center of the tracked areas is considered as the object's position. The proposed algorithm is evaluated on several classical videos. The experimental results show that the regression model weighted multi-templates MS algorithm can track an object accurately in terms of occlusion, illumination changes and pose variations.

• 전기학회논문지
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• 제63권11호
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• pp.1551-1558
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• 2014

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

• 스마트미디어저널
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• 제13권5호
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• pp.9-18
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• 2024

객체 궤적 모델링은 다중 객체 추적(Multi Object Tracking, MOT)의 주요 과제다. CenterTrack은 객체 중심 위치를 추적하는 Heatmap 기반의 방법으로 이를 해결하고자 했다. 하지만 복잡한 움직임과 비선형성을 가진 객체를 추적할 때 제한적인 성능을 보였다. 우리는 CenterTrack의 성능 저하 요인을 보행자의 동적 움직임으로 간주하여 확장된 칼만 필터(Extended Kalman Filter, EKF)를 CenterTrack에 통합했다. 우리가 제안하는 방법의 우수성을 입증하기 위해 기존 칼만 필터(Kalman Filter, KF)와 무향 칼만 필터(Unscented Kalman Filter, UKF)를 CenterTrack에 적용 후 다양한 데이터셋에 비교 평가했다. 실험결과, EKF를 CenterTrack에 통합했을 때 73.7% MOTA(Multiple Object Tracking Accuracy)를 달성하며 CenterTrack에 가장 적합한 필터임을 확인했다.

• 반도체디스플레이기술학회지
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• 제14권3호
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• pp.67-72
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• 2015

This paper proposes an object tracking system based on keypoints using homography in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information. Tracking module tracks an object using homography information that generate by being matched on the learned object keypoints to the current object keypoints. Then update the window included the object for defining object's pose. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track objects with updating object's pose for the use of mobile platform.