Earticle

초록

영어

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose subframe analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed for full frame image. We reduced its computational requirement significantly without losing throughput and object detection accuracy with the proposed method.

목차

Abstract
1. INTRODUCTION
2. RELATED WORKS
2.1 Object Detection Algorithm
2.2 Correlation based Object Tracking
2.3 Object Detection and Tracking as an Edge Service
3. PROPOSED OBJECT DETECTION USING SUB-FRAME ANALYSIS
3.1 Object Detection and Tracking
3.2 Sub-Frame Analysis
3.3 Sub-Frame Analysis based Object detection and tracking
4. PERFORMANCE EVALUATION
4.1 Setup and evaluation
4.2 Speed Analysis
4.3 Qualitative evaluation
5. CONCLUSION

키워드

저자

• Bum-Suk Jang [ BS SOFT Co., LTD ]
• Sang-Hyun Lee [ Department of Computer Engineering, Honam University, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380