• 반도체디스플레이기술학회지
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• 제17권2호
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• pp.67-70
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• 2018

Object detection and tracking is an exciting and interesting research area in the field of computer vision, and its technologies have been widely used in various application systems such as surveillance, military, and augmented reality. This paper proposes and implements a novel and more robust object recognition and tracking system to localize and track multiple objects from input images, which estimates target state using the likelihoods obtained from multiple CNNs. As the experimental result, the proposed algorithm is effective to handle multi-modal target appearances and other exceptions.

• 전자공학회논문지SC
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• 제37권3호
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• pp.1-8
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• 2000

이 논문에서는 NN 필터를 이용한 표적추적을 위한 최적의 신호 강도 및 표적 검출 문턱값을 구하였다. 이를 위하여 먼저 HYCA 방식을 이용하여 NN 필터의 추적성능을 예측할 수 있도록 하고, 이것에 기초하여 예측된 추적성능과 신호 강도 및 표적 검출 문턱값 사이의 관계를 나타내었다. 그리고 이러한 관계를 이용하여 다음과 같은 다양한 비용에 대한 최적 파라미터를 얻었다： (1)위치 추정 오차 분산 합을 최소화하는 최적의 표적 검출 문턱값 순열(sequence)； (2)유효 게이트 면적 합을 최소화하는 최적의 표적 검출 문턱값 순열； (3)표적 신호 강도 합을 최소화하는 최적 표적 신호 강도 및 표적 검출 문턱값 순열.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2008년도 하계종합학술대회
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• pp.1211-1212
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• 2008

In this paper, we propose image process modeling as a part of location based surveillance system for unauthorized target recognition and tracking in harbor, airport, military zone. For this, we compress and store background image in lower resolution and perform object extraction and motion tracking by using sobel edge detection and difference picture method between real images and a background image. In addition to, we use Independent Component Analysis Neural Network for moving target recognition. Experiments are performed for object extraction and tracking of moving targets on road by using static camera in 20m height building and it shows the robust results.

• 전기학회논문지P
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• 제64권3호
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• pp.187-192
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• 2015

Detecting human in images is a challenging task owing to their variable appearance and the wide range of poses the they can adopt. The first need is a robust feature set that allows the human form to be discriminated cleanly, even in cluttered background under difficult illumination. A large number of vision application rely on matching keypoints across images. These days, the deployment of vision algorithms on smart phones and embedded device with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster compute, more compact while remaining robust scale, rotation and noise. In this paper we focus on improving the speed of pedestrian(walking person) detection using Histogram of Oriented Gradient(HOG) descriptors provide excellent performance and tracking using kalman filter.

• 한국정보통신학회논문지
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• 제17권12호
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• pp.3023-3029
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• 2013

일반적으로 함정은 표적을 탐지하고 추적하는 기능을 하는 다양한 장비를 보유하고 있으며 각 장비들 간의 정보교류를 통해 보다 정확하고 신속하게 대상 표적을 추적하고 있다. 이런 장비들은 대체로 유사한 표적탐색영역(FOV)을 보유하지만 일부는 해당 장비의 오차범위(Resolution) 한계로 인해 장비간의 차이가 발생하기도 한다. 본 논문에서는 전자광학추적장비(Electro Optic Tracking System)와 레이더 시스템 간의 표적탐색영역(FOV) 차이를 보상하기 위해 사용된 전자광학추적장비 표적탐색 방식을 랜덤한 표적정보를 기준으로 다양한 방법을 통해 탐색시간을 단축하고, 자동으로 표적을 탐지/추적할 수 있는 방법에 대해 연구하였다.

• 전자공학회논문지D
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• 제34D권7호
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• pp.88-96
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• 1997

As the need of automatized system has been increased recently together with the development of industrial and military technologies, the adaptive real-time target detection technologies that can be embedded on vehicles, planes, ships, robots and so on, are hgihly demanded. Accordingly, this paper proposes a novel approach to detect and segment the moving targets using the binary phase extraction joint transform correlator (BPEJTC), the advanced image subtraction filter and convex hull processing. The BPEJTC which was used as a target detection unit mainly for target tracking compensating the camera movement. The target region has been detected by processing the successful three frames using the advanced image subtraction filter, and has become more accurate by applying the developed convex hull filter. As shown by some experimental results, it is expected that the proposed approaches for compensation of the camera movement and segmentationof of target region, can be used for th emissile guiddance, aero surveillance, automatic inspectin system as well as the target detection unit of automatic target recognition system that request adaptive real-time processing.

• 제어로봇시스템학회논문지
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• 제9권10호
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• pp.747-754
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• 2003

An enhancement of the probabilistic data association filter is presented for tracking a single maneuvering target in clutter environment. The use of the variable dimensional structure leads the probabilistic data association filter to adjust to real motion of a target. The detection of the maneuver for the model switching is performed by the acceleration estimates taken from a bias estimator of the two stage Kalman filter. The proposed algorithm needs low computational power since it is implemented with a single filtering procedure. A simple Monte Carlo simulation was performed to compare the performance of the proposed algorithm and the IMMPDA filter.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.2067-2078
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• 2017

In this paper, a new face tracking algorithm is proposed. The CamShift (Continuously adaptive mean SHIFT) algorithm shows unstable tracking when there exist objects with similar color to that of face in the background. This drawback of the CamShift is resolved by the proposed algorithm using Kinect's pixel-by-pixel depth information and the skin detection method to extract candidate skin regions in HSV color space. Additionally, even when the target face is disappeared, or occluded, the proposed algorithm makes it robust to this occlusion by the feature point matching. Through experimental results, it is shown that the proposed algorithm is superior in tracking performance to that of existing TLD (Tracking-Learning-Detection) algorithm, and offers faster processing speed. Also, it overcomes all the existing shortfalls of CamShift with almost comparable processing time.

• 산업기술연구
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• 제21권B호
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• pp.91-98
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• 2001

In this paper, center detection and motion analysis of a moving object are studied. Kohonen's self-organizing neural network models are used for the moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation. It is possible to distinguish 8 directions of a moving trajectory with two frames and 16 directions with three frames.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.99.6-99
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• 2001

The parameter optimization for agile beam radar tracking is addressed to minimize the radar resources that are required to maintain a target under track. The parameters to be optimized include the track-revisit interval and the sequence of pairs of target signal strengths and detection thresholds associated with repeated illumination attempts in each track-revisit. The optimization problem is solved numerically for typical examples.