Earticle

초록

영어

Public surveillance monitoring is rapidly finding its way into Intelligent Surveillance System. Street crime is increasing in recent years, which has demanded more reliable and intelligent public surveillance system. In this paper, the ability and the accuracy of an Adaptive Neuro-Fuzzy Inference System (ANFIS) was investigated for the classification of moving objects for street scene applications. The goal of this paper is to classify the moving objects prior to its communal attributes that emphasize on three major processes which are object detection, discriminative feature extraction, and classification of the target. The intended surveillance application would focus on street scene, therefore the target classes of interest are pedestrian, motorcyclist, and car. The adaptive network based on Neuro-fuzzy was independently developed for three output parameters, each of which constitute of three inputs and 27 Sugeno-rules. Extensive experimentation on significant features has been performed and the evaluation performance analysis has been quantitatively conducted on three street scene dataset, which differ in terms of background complexity. Experimental results over a public dataset and our own dataset demonstrate that the proposed technique achieves the performance of 93.1% correct classification for street scene with moving objects, with compared to the solely approaches of neural network or fuzzy.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Proposed Algorithm
  3.1. Moving Object Detection
  3.2. Features Extraction
  3.3. Moving Object Classification
 4. Results and Discussion
  4.1. Accuracy Analysis of Proposed Method
  4.2. Comparison Result
 5. Conclusion
 Acknowledgment
 References

키워드

저자

• M. A. Rashidan [ Department of Mechatronics Faculty of Engineering, International Islamic University Malaysia (IIUM) Jalan Gombak, 53100 Kuala Lumpur, Malaysia. ]
• Y. M. Mustafah [ Department of Mechatronics Faculty of Engineering, International Islamic University Malaysia (IIUM) Jalan Gombak, 53100 Kuala Lumpur, Malaysia. ]
• A. A. Shafie [ Department of Mechatronics Faculty of Engineering, International Islamic University Malaysia (IIUM) Jalan Gombak, 53100 Kuala Lumpur, Malaysia. ]
• N. A. Zainuddin [ Department of Mechatronics Faculty of Engineering, International Islamic University Malaysia (IIUM) Jalan Gombak, 53100 Kuala Lumpur, Malaysia. ]
• N. N. A. Aziz [ Department of Mechatronics Faculty of Engineering, International Islamic University Malaysia (IIUM) Jalan Gombak, 53100 Kuala Lumpur, Malaysia. ]
• A. W. Azman [ Department of Electrical and Computer Engineering Faculty of Engineering, International Islamic University Malaysia (IIUM) Jalan Gombak, 53100 Kuala Lumpur, Malaysia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605