본문 바로가기
  • 학술저널

표지

DBpia에서 서비스 중인 논문에 한하여 피인용 수가 반영됩니다. 내서재에 논문을 담은 이용자 수의 총합입니다.

초록·키워드 목차

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques. #Object detection #HOG #SVM #CNN

Abstract
I. Introduction
II. Related works
III. Research motives
IV. The Proposed Scheme
V. Experiment Results
Ⅵ. Conclusions and future work
REFERENCES

저자의 논문

DBpia에서 서비스 중인 논문에 한하여 피인용 수가 반영됩니다.
논문의 정보가 복사되었습니다.
붙여넣기 하세요.