• Title/Summary/Keyword: 오클루전

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A Study on Synthetic Techniques Utilizing Map of 3D Animation - A Case of Occlusion Properties (오클루전 맵(Occlusion Map)을 활용한 3D애니메이션 합성 기법 연구)

  • Park, Sung-Won
    • Cartoon and Animation Studies
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    • s.40
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    • pp.157-176
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    • 2015
  • This research describes render pass synthetic techniques required to use for the effectiveness of them in 3D animation synthetic technology. As the render pass is divided by property and synthesized after rendering, elaborate, rapid synthesis can be achieved. In particular, occlusion pass creates a screen as if it had a soft, light shading, expressing a sense of depth and boundary softness. It is converted into 2D image through a process of pass rendering of animation projects created in 3D space, then completed in synthetic software. Namely, 3D animation realizes the completeness of work originally planned through compositing, a synthetic process in the last half. To complete in-depth image, a scene manufactured in 3D software can be sent as a synthetic program by rendering the scene by layer and property. As recently the occlusion pass can express depth notwithstanding conducting GI rendering of 3D graphic outputs, it is an important synthetic map not omitted in the post-production process. Nonetheless, for the importance of it, currently the occlusion pass leaves much to be desired for research support and books summarizing and analyzing the characteristics of properties, and the principles and usages of them. Hence, this research was aimed to summarize the principles and usages of occlusion map, and analyze differences in the results of synthesis. Furthermore, it also summarized a process designating renderers and the map utilizing the properties, and synthetic software usages. For the future, it is hoped that effective and diverse latter expression techniques will be studied beyond the limitation of graphic expression based on trends diversifying technique development.

여러 대의 카메라를 이용한 계층적 깊이정보 추출 알고리즘

  • 박종일;이노우에세이키
    • Broadcasting and Media Magazine
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    • v.2 no.1
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    • pp.45-56
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    • 1997
  • 이 논문에서는 여러대의 카메라를 이용하여, 화면내의 모든 화소에 대해 정확한 깊이정보를 추출하는 알고리듬을 제안한다. 제안방법은 스테레오정합에 기반하고 있다. 스트레오정압에 의해 깊이정보를 추정하는데 있어서는 , 오클루전(occlusion)이 추정에 미치는 악영향을 어떻게 극복하는가가매우 중요한 문제이다. 오클루전문제는 2대의 카메라로는 근본적으로 해결이 불가능하다. 따라서 이논문에서는 5대의 카메라를 사용하여, 각 카메라로부터의 정보를 선택적으로 사용함으로써, 이 문제를 상당히 개선한다. 한편, 스테레오 정합법에서는 정합창의 크기가 추정성능에 영향을 미치는데, 큰 창을 이요하면 잡음내성은 우수하나 깊이가 불연속인 곳에서 오차가 발생하고, 작은창을 이용하면 잡음성능은 저하되나 불연속부에서의 창크기에 의한 오차가 줄어드는, 대차관계가 존재한다. 이 논문에서는 처음에 큰 창으로 추정하고 차츰 창의 크기를 줄여나가는 계층적 방법을 제안하여, 잡음내성을 강화하고 불연속부의 오차를 줄이고 있다. 실험경과를 통하여 제안방법의 성능을 제시하고 타당성을 확인한다.

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Video Analysis System for Action and Emotion Detection by Object with Hierarchical Clustering based Re-ID (계층적 군집화 기반 Re-ID를 활용한 객체별 행동 및 표정 검출용 영상 분석 시스템)

  • Lee, Sang-Hyun;Yang, Seong-Hun;Oh, Seung-Jin;Kang, Jinbeom
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.89-106
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    • 2022
  • Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.