Efficient Swimmer Detection Algorithm using CNN-based SVM

Dasol Hong; Kim， yoon

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2017, 22(12), pp.79-85
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Dasol Hong ¹, Kim， yoon ²

¹강원대학교
²강원대학교

ABSTRACT

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.
KEYWORDS

Object detection, HOG, SVM, CNN
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