• Agribusiness and Information Management
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• v.8 no.1
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• pp.1-10
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• 2016

Information and communication technologies (ICTs) have always mattered in agriculture too. In day-to-day practices of agriculture and allied sectors, the farmers often share their information. Changing weather patterns, soil conditions, pests and diseases always throw challenges to small and marginal farmers. So, the farmer needs up-dated information to cope with and even benefit from these changes. In the developing countries like India, where agriculture still plays a crucial role (over 58% of the rural households depend on agriculture as their livelihood) and the rising population from 1027 million to 1419 million during 2001-16 (a total rise of 38 percent or 1.3 percent per year) pose a lot of pressure on land and other resources to meet the food security needs on one hand and to meet the challenges of globalization on the other. Understanding and addressing these challenges are very crucial, in which ICT can play a major role. With the booming mobile, wireless, and Internet industries, ICT has found a foothold even in poor marginal and smallholder farms and in their activities. The survey conducted among the 120 farmers in Srikakulam district in India revealed that, ICT has revolutionized the agriculture in the modern days. Production and marketing information is accessed by 91% of the sample farmers through mobile in 2015, where it was only 5% in 2005. The extent of use of mobile phones by the farmers varied with the decision to be taken by them like Harvesting, packing, and storing (94%), Selling Decision (91%), Seed purchase (89%), Application of fertilizers and pesticides (88%) and Land preparation and planting (84%), other package of practices (77%). The farmers further opined that, 'Voice' was the dominating source of communication (96%) compared to Short Message Service (SMS) (only 27%) and Internet access (10%), as majority are illiterate. The use of camera (71%), Bluetooth (33%), Radio (61%) TV (41%) are the other means of sharing the information. In this context of importance of ICTs in Indian agriculture, greater attention justifies about the applications of ICT's to alleviate poverty and promote economic growth of the farming population.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.12
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• pp.123-129
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• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.65-72
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• 2009

This paper presents a vision based localization method for indoor mobile robots using the types of vertices from a monocular image. In the images captured from a camera of a robot, the types of vertices are determined by searching vertical edges and their branch edges with a geometric constraints. For obtaining correspondence between the comers of a 2-D map and the vertex of images, the type of vertices and geometrical constraints induced from a geometric analysis. The vertices are matched with the comers by a heuristic method using the type and position of the vertices and the comers. With the matched pairs, nonlinear equations derived from the perspective and rigid transformations are produced. The pose of the robot is computed by solving the equations using a least-squares optimization technique. Experimental results show that the proposed localization method is effective and applicable to the localization of indoor environments.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.95-101
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• 2023

Colmap is one of the innovative artificial intelligence technologies, highly effective as a tool in 3D reconstruction tasks. Moreover, it excels at constructing intricate 3D models by utilizing images and corresponding metadata. Colmap generates 3D models by merging 2D images, camera position data, depth information, and so on. Through this, it achieves detailed and precise 3D reconstructions, inclusive of objects from the real world. Additionally, Colmap provides rapid processing by leveraging GPUs, allowing for efficient operation even within large data sets. In this paper, we have presented a method of collecting 2D images of traditional Korean towers and reconstructing them into 3D models using Colmap. This study applied this technology in the restoration process of traditional stone towers in South Korea. As a result, we confirmed the potential applicability of Colmap in the field of cultural heritage restoration.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.290-293
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• 2021

In this paper, we implement a learning efficiency verification system to inspire learning motivation and help improve concentration by detecting the situation of the user studying. To this aim, data on learning attitude and concentration are measured by extracting the movement of the user's face or body through a real-time camera. The Jetson board was used to implement the real-time embedded system, and a convolutional neural network (CNN) was implemented for image recognition. After detecting the feature part of the object using a CNN, motion detection is performed. The captured image is shown in a GUI written in PYQT5, and data is collected by sending push messages when each of the actions is obstructed. In addition, each function can be executed on the main screen made with the GUI, and functions such as a statistical graph that calculates the collected data, To do list, and white noise are performed. Through learning efficiency checking system, various functions including data collection and analysis of targets were provided to users.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.217-224
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• 2024

With the rapid growth of K-POP, the dance content industry is spreading. With the recent increase in the spread of SNS, they also shoot and share their dance videos. However, it is not easy for dance beginners who are new to dancing to learn dance moves because it is difficult to receive objective feedback when dancing alone while watching videos. This paper describes a system that uses MediaPipe to compare choreography videos and dance videos of users and detect whether they are following the movement correctly. This study proposes a method of giving feedback based on Color Map to users by calculating the similarity of dance movements between user images taken with webcam or camera and choreography images using cosine similarity and COCO OKS. Through this system, objective feedback on users' dance movements can be visually received, and beginners are expected to be able to learn accurate dance movements.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.125-139
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• 2008

The purpose of this study is to compare and analyze a kinematic characteristics of throwing performance among born visually impaired, postnatal visually impaired and visually correct people through their kinematic characteristics. Another purpose of this study is to prevent injury and improve blind's exercise leadership and physical abilities. Three video cameras were used and each camera's shooting velocity was 60field/sec. Then the longest thrown pose was collected and analyzed by using Kwon 3D ver. 3.1 program. The results obtained from this study were followings; 1. During release, the fastest speed of ball was visually corrected followed by postnatal visually impaired and then born visually impaired. 2. Visually corrected used wrist joint well and had wide body round range with similar motion patterns, however born visually impaired couldn't do this at all. 3. Visually corrected used waist to lead shoulder at Phase 2 while throwing ball, but some of born visually impaired and postnatal visually impaired couldn't use waist to lead shoulder. 4. Visually corrected looked at the way where ball would go, but blinds couldn't look where ball would go. 5. Not like visually corrected who show similar patterns, born visually impaired showed each different patterns and some postnatal visually impaired showed similar patterns with visually corrected. 6. While throwing ball, visually corrected showed flexible weight-movement, postnatal visually impaired were not good as visually corrected, and born visually impaired couldn't do weight-movement at all. Synthetically, this result showed that throwing motions are affected a lot by the time when people became blind.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.345-354
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• 2010

Visual tracking techniques for Augmented Reality are classified as either a marker tracking approach or a natural feature tracking approach. Marker-based tracking algorithms can be efficiently implemented sufficient to work in real-time on mobile devices. On the other hand, natural feature tracking methods require a lot of computationally expensive procedures. Most previous natural feature tracking methods include heavy feature extraction and pattern matching procedures for each of the input image frame. It is difficult to implement real-time augmented reality applications including the capability of natural feature tracking on low performance devices. The required computational time cost is also in proportion to the number of patterns to be matched. To speed up the natural feature tracking process, we propose a novel fast tracking method based on optical flow. We implemented the proposed method on mobile devices to run in real-time and be appropriately used with mobile augmented reality applications. Moreover, during tracking, we keep up the total number of feature points by inserting new feature points proportional to the number of vanished feature points. Experimental results showed that the proposed method reduces the computational cost and also stabilizes the camera pose estimation results.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.183-190
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• 2016

This paper proposes algorithm to reduce the computing time in a neural network that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. Object Detection can be defined as follows : Given image sequence, which can forom a digitalized image, the goal of object detection is to determine whether or not there is any object in the image, and if present, returns its location, direction, size, and so on. But object in an given image is considerably difficult because location, size, light conditions, obstacle and so on change the overall appearance of objects, thereby making it difficult to detect them rapidly and exactly. Therefore, this paper proposes fast and exact object detection which overcomes some restrictions by using neural network. Proposed system can be object detection irrelevant to obstacle, background and pose rapidly. And neural network calculation time is decreased by reducing input vector size of neural network. Principle Component Analysis can reduce the dimension of data. In the video input in real time from a CCTV was experimented and in case of color segment, the result shows different success rate depending on camera settings. Experimental results show proposed method attains 30% higher recognition performance than the conventional method.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.307-312
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• 2016

Markerless tracking for augmented reality using Homography can augment virtual objects correctly and naturally on live view of real-world environment by using correct pose and direction of camera. The RANSAC algorithm is widely used for estimating Homography. CS-RANSAC algorithm is one of the novel algorithm which cooperates a constraint satisfaction problem(CSP) into RANSAC algorithm for increasing accuracy and decreasing processing time. However, CS-RANSAC algorithm can be degraded performance of calculating Homography that is caused by selecting feature points which estimate low accuracy Homography in the sampling step. In this paper, we propose feature point filtering method based on CS-RANSAC for efficient planar Homography estimating the proposed algorithm evaluate which feature points estimate high accuracy Homography for removing unnecessary feature point from the next sampling step using Symmetric Transfer Error to increase accuracy and decrease processing time. To evaluate our proposed method we have compared our algorithm with the bagic CS-RANSAC algorithm, and basic RANSAC algorithm in terms of processing time, error rate(Symmetric Transfer Error), and inlier rate. The experiment shows that the proposed method produces 5% decrease in processing time, 14% decrease in Symmetric Transfer Error, and higher accurate homography by comparing the basic CS-RANSAC algorithm.