• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권2호
• /
• pp.710-726
• /
• 2018

This paper presents an extended particle filter to increase the accuracy and decrease the computation load of vehicle tracking. Particle filter has been the subject of extensive interest in video-based tracking which is capable of solving nonlinear and non-Gaussian problems. However, there still exist problems such as preventing unnecessary particle consumption, reducing the computational burden, and increasing the accuracy. We aim to increase the accuracy without an increase in computation load. In proposed method, we calculate the direction angle of the target vehicle. The angular difference between the direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted, based on their angular difference. Particles with angular difference greater than a threshold is eliminated and the remaining are stored with greater weights in order to increase their probability for state estimation. Threshold value is very critical for performance. Thus, instead of having a constant threshold value, proposed algorithm updates it online. The first advantage of our algorithm is that it prevents the system from failures caused by insufficient amount of particles. Second advantage is to reduce the risk of using unnecessary number of particles in tracking which causes computation load. Proposed algorithm is compared against camshift, direction-based particle filter and condensation algorithms. Results show that the proposed algorithm outperforms the other methods in terms of accuracy, tracking duration and particle consumption.

• ETRI Journal
• /
• 제38권2호
• /
• pp.356-365
• /
• 2016

This research proposes a modified particle filter to increase the accuracy of vehicle tracking in a noisy and occluded medium. In our proposed method for vehicle tracking, the direction angle of a target vehicle is calculated. The angular difference between the motion direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted depending on their angular distance to the motion direction. Those particles moving in a direction similar to that of the target vehicle are assigned larger weights; this, in turn, increases their probability in a given likelihood function (part of the process of estimation of a target's state parameters). The proposed method is compared against a condensation algorithm. Our results show that the proposed method improves the stability of a particle filter tracker and decreases the particle consumption.

• 한국HCI학회:학술대회논문집
• /
• 한국HCI학회 2009년도 학술대회
• /
• pp.186-192
• /
• 2009

본 논문은 비전을 이용한 인간 정면 상반신 포즈를 인식 방법에 대해서 기술한다. 일반적으로 HCI(Human Computer Interaction)와 HRI(Human Robot Interaction)에서는 인간이 정면을 바라볼 때 얼굴, 손짓으로 의사소통 하는 경우가 많기 때문에 본 논문에서는 인식의 범위를 인간의 정면 그리고 상반신에 대해서만 한정한다. 인간 포즈인식의 주요 두 가지 어려움은 첫째 인간은 다양한 관절로 이루어진 객체이기 때문에 포즈의 자유도가 높은 문제점 때문에 모델링이 어렵다는 것이다. 둘째는 모델링된 정보와 영상과의 매칭이 어려운 것이다. 이를 해결하기 위해 본 논문에서는 모델링이 쉬운 그림모델(Pictorial Model)을 이용해 인체를 다수 사각형 파트로 모델링 하였고 이를 이용해 주요한 상반신 포즈를 DB화 해 인식한다. DB 포즈로 표현되지 못하는 세부포즈는 인식된 주요 포즈 파라미터로 부터 파티클필터를 이용해 예측한 다수 파티클로부터 가장 높은 사후분포를 갖는 파티클을 찾아 주요 포즈를 업데이트하여 결정한다. 따라서 주요한 포즈 인식과 이를 기반으로 한 세부 포즈를 추적하는 두 단계를 통해 인체 정면 상반신 포즈를 정확하게 인식 할 수 있다.

• 한국입자에어로졸학회지
• /
• 제13권2호
• /
• pp.97-104
• /
• 2017

In order to characterize atmospheric aerosols in Chungcheong area, black carbon concentration, which is known to be closely related to global warming, was measured and compared with $PM_{10}$, $PM_{2.5}$ concentrations and various meteorological parameters such as wind velocity and wind direction. Multi Angle Absorption Photometer (MAAP), a filter-based equipment, was used for the black carbon measurement, and the $PM_{10}$, $PM_{2.5}$ concentrations, wind velocity and wind direction were provided by the local monitoring stations. Black carbon concentration was monitored to be high in spring and winter but low in fall. $PM_{10}$ concentration was observed to be high when westerly wind was strong.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제17권5호
• /
• pp.1339-1355
• /
• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• 전자공학회논문지
• /
• 제51권6호
• /
• pp.102-109
• /
• 2014

입자 시스템은 물리적 현상을 모델링하기 위해 자주 사용된다. 특히, 3차원 공간에서의 풍경, 구름, 파도, 안개, 비, 눈, 불꽃 등의 모델링에 적합하다. 시뮬레이션 모델링에는 다양한 전통적인 방법이 존재하지만 본 논문에서는 입자 시스템을 사용하여 불꽃 입자 추적을 기반으로 한 새로운 불꽃 모델링 기법을 제시하였다. 이 방법은 불꽃 추적을 통해 발사 및 분산한 입자들을 인식하고, 스테레오 기법을 이용함으로써 3D 깊이 값을 구하여 비교적 정확한 3차원적 위치를 추출 할 수 있다. 그러므로 불꽃 입자의 위치, 속도, 색상 및 수명 등의 파라메타를 불꽃 추적을 통해 산출하였고 이를 이용하여 3D 시뮬레이션을 재연할 수 있다. 본 연구는 빠른 입자 추출 및 노이즈에 의한 허위 입자 추출을 방지하기 위해 관심 영역을 사용하였고, 발사 단계에서 견고성을 향상시키기 위해 칼만 필터를 사용하였다. 또한, 입자의 이동 방향을 예측하여 효율적인 추적을 위해 입자의 최대 이동 범위를 고려한 새로운 불꽃 입자 추적 방법을 제안 하였다. 그리고 3D 공간에서 입자의 속도는 불꽃의 회전 각도를 찾음으로써 얻어 질 수 있다. 본 논문에서는 불꽃축제에서 자주 사용되는 구, 원, 국화, 하트 이 네 가지 불꽃 유형에 대하여 각각 모델링에 필요한 파라메타를 불꽃 추적을 통해 구하였고 추적에 대한 속도와 정확도를 측정하였다.

• 전기학회논문지
• /
• 제65권6호
• /
• pp.1060-1069
• /
• 2016

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

• 한국대기환경학회지
• /
• 제32권3호
• /
• pp.272-279
• /
• 2016

Understanding sources and contributions of $PM_{2.5}$ mass and particulate PAHs from traffic-related pollution can provide valuable information for alleviating air contamination from car emissions in urban areas. Two sampling sites at the Gwangju Institute of Science and Technology (GIST, $35.228^{\circ}N$, $126.843^{\circ}E$) and National institute of environmental research NamBu Supersite (NNBS, $35.226^{\circ}N$, $126.848^{\circ}E$) were selected for comprehensive road-oriented-PM investigations. Continuous measurements from optical particle sizer (OPS) and optical particle counter (OPC) with 24 hr integrated filter based samplers for organic carbon, water soluble organic carbon, and Poly Aromatic Hydrocarbons (PAHs) were conducted during Nov. 3 through 22 in 2014. As a result, $PM_{2.5}$ mass concentrations using OPC and OPS in NNBS presented about twice higher than in GIST due to road dust impacts based on wind direction analysis. In addition, ratios of elemental carbon (EC) to organic carbon (OC) and water insoluble organic carbon (WIOC) to organic carbon (OC) supported an additional evidence of the primary pollutant contributions oriented from road dust. PAHs related to 5 rings such as benzo(e&a)pyrene indicates higher associations.