• Journal of Information Processing Systems
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• 제17권2호
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.555-555
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• 2000

This study is concentrated on the development of hight speed multi-object image processing algorithm, and based on these a1gorithm, vision control scheme is developed for the robot's position control in real time. Recently, the use of vision system is rapidly increasing in robot's position centre. To apply vision system in robot's position control, it is necessary to transform the physical coordinate of object into the image information acquired by CCD camera, which is called image processing. Thus, to control the robot's point position in real time, we have to know the center point of object in image plane. Particularly, in case of rigid body, the center points of multi-object must be calculated in a image plane at the same time. To solve these problems, the algorithm of multi-object for rigid body control is developed.

• 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.

• 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.

• 로봇학회논문지
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• 제12권1호
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• pp.33-41
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• 2017

We present a region-based approach for accurate pose estimation of small mechanical components. Our algorithm consists of two key phases: Multi-view object co-segmentation and pose estimation. In the first phase, we explain an automatic method to extract binary masks of a target object captured from multiple viewpoints. For initialization, we assume the target object is bounded by the convex volume of interest defined by a few user inputs. The co-segmented target object shares the same geometric representation in space, and has distinctive color models from those of the backgrounds. In the second phase, we retrieve a 3D model instance with correct upright orientation, and estimate a relative pose of the object observed from images. Our energy function, combining region and boundary terms for the proposed measures, maximizes the overlapping regions and boundaries between the multi-view co-segmentations and projected masks of the reference model. Based on high-quality co-segmentations consistent across all different viewpoints, our final results are accurate model indices and pose parameters of the extracted object. We demonstrate the effectiveness of the proposed method using various examples.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.332-336
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• 1992

We describe about the graphic simulation system which supports the determination of efficient multi-robot motions during cooperation. For the construction of the simulation software for multi-robot motions, two problems are presented. First problem is that all the robot motions must be determinded using both the desired object motions and the holonomic constraints with the object. To find the robot motions combined with the various object motion path, the robot motions are derived from the desired object path instead of a master robot path. Therefore robot motions can be easily modifiable with the various object motions. This type of motion determination is different from that of the master-slaves method using the master robot motions. The other is that the developments of robot application softwares need a heavy cost when the participated robots or the tasks given to the robots are changed. Based on object-oriented programming paradigm, we present useful software objects describing robot application programming environment. The object-oriented programming paradigm increases the software reusability, reliability, and extensibility, and also provides the structual concepts to cope with the various demands of robot application programming.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.395-405
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• 2012

A joint template matching algorithm is proposed in this paper to reduce the high rate of miss-detection and false-alarm caused by the traditional template matching algorithm during the process of multi-object detection. The proposed algorithm can reduce the influence on each object by matching all objects together according to the correlation information among different objects. Moreover, the rate of miss-detection and false-alarm in the process of single-template matching is also reduced based on the algorithm. In this paper, firstly, joint template is created from the information of relative positions among different objects. Then, matching criterion according to normalized cross correlation is generated for multi-object matching. Finally, the proposed algorithm is applied to the detection of watermarks in bill. The experiments show that the proposed algorithm has lower miss-detection and false-alarm rate comparing to the traditional NCC algorithm during the process of multi-object detection.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.439-449
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• 2023

This paper deals with a resource efficient artificial intelligence (AI) service architecture for multi-channel video streams. As an AI service, we consider the object detection model, which is the most representative for video applications. Since most object detection models are basically designed for a single channel video stream, the utilization of the additional resource for multi-channel video stream processing is inevitable. Therefore, we propose a resource efficient AI service framework, which can be associated with various AI service models. Our framework is designed based on the modular architecture, which consists of adaptive frame control (AFC) Manager, multiplexer (MUX), adaptive channel selector (ACS), and YOLO interface units. In order to run only a single YOLO process without regard to the number of channels, we propose a novel approach efficiently dealing with multi-channel input streams. Through the experiment, it is shown that the framework is capable of performing object detection service with minimum resource utilization even in the circumstance of multi-channel streams. In addition, each service can be guaranteed within a deadline.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제9권6호
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• pp.712-717
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• 2003

자연배경으로부터 불특정 다수 객체의 윤곽선들을 자동 추출하는 다중 스네이크(Snake) 알고리즘을 제안하였다. 먼저 잡음에 강건한 문맥자유 주목연산자(context-free attention operator)를 이용하여 자연배경에 혼재하는 불특정 다수 객체들을 자동 검출하고, 각 객체별로 스네이크의 초기 윤곽들을 자동 설정함으로써 기존 스네이크 알고리즘에서는 어려웠던 초기 윤곽의 자동 설정과 여러 객체 윤곽선의 동시 추출 문제를 해결하였다. 이때 각 스네이크의 초기 윤곽들은 기존의 방법들에 비해 객체들의 실제윤곽선에 좀 더 가까이 설정하여 요철이 큰 객체들의 윤곽선도 쉽게 추출 할 수 있도록 하였다. 다양한 합성 영상과 자연배경의 실영상에 대해 실험하여 잡음이 있는 복잡한 배경으로부터도 불특정 다수 객체의 윤곽선을 효과적으로 자동 추출함을 확인하였다.