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Paper Abstract and Keywords
Presentation 2019-12-20 10:45
An Efficient Block-wise Object Detection Method using Consecutive Frames for High Resolution Video
Kazuki Hozumi, Yoichi Tomioka (UoA) PRMU2019-57
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, in the fields such as surveillance cameras and in-vehicle camera systems, efficient deep-learning-based object detection methods, such as Single Shot MultiBox Detector (SSD), that do not require window scanning have received a significant attention. However, these methods require a lot of memory and computation. For this reason, when we apply them to higher definition video, it can be necessary to divide the video into multiple blocks for inference processing due to restrictions on memory capacity of GPUs or FPGAs. However, the detection accuracy of objects near the block division boundary can be low. Although we can use overlap blocks to reduce the effects of block boundary, it increases the number of blocks and execution time. In this paper, we propose a method for reducing the execution time per frame, which assigns a different pattern to each fame and integrates the results of object detection from multiple frames. In the experiments, the object detection accuracy was evaluated using three data from the Multiple Object Tracking Benchmark dataset 2017. We reduced the number of blocks per frames to 55.6% while the accuracy denegeration is within 4.5%.
Keyword (in Japanese) (See Japanese page) 
(in English) Convolutional neural network / Deep learning / Object detection / Single Shot Multibox Detector Detector / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 347, PRMU2019-57, pp. 69-74, Dec. 2019.
Paper # PRMU2019-57 
Date of Issue 2019-12-12 (PRMU) 
ISSN Online edition: ISSN 2432-6380
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee PRMU  
Conference Date 2019-12-19 - 2019-12-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To PRMU 
Conference Code 2019-12-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An Efficient Block-wise Object Detection Method using Consecutive Frames for High Resolution Video 
Sub Title (in English)  
Keyword(1) Convolutional neural network  
Keyword(2) Deep learning  
Keyword(3) Object detection  
Keyword(4) Single Shot Multibox Detector Detector  
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1st Author's Name Kazuki Hozumi  
1st Author's Affiliation University of Aizu (UoA)
2nd Author's Name Yoichi Tomioka  
2nd Author's Affiliation University of Aizu (UoA)
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Speaker Author-1 
Date Time 2019-12-20 10:45:00 
Presentation Time 15 minutes 
Registration for PRMU 
Paper # PRMU2019-57 
Volume (vol) vol.119 
Number (no) no.347 
Page pp.69-74 
#Pages
Date of Issue 2019-12-12 (PRMU) 


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