Moving object detection and classification using neuro-fuzzy approach
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 Sy...
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Science and Engineering Research Support Society
2016
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| Online Access: | http://irep.iium.edu.my/53263/ http://irep.iium.edu.my/53263/ http://irep.iium.edu.my/53263/1/4.%20Journal_IJMUE.pdf |
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iium-532632017-07-24T07:40:52Z http://irep.iium.edu.my/53263/ Moving object detection and classification using neuro-fuzzy approach Rashidan, M. Ariff Mohd Mustafah, Yasir Shafie, Amir Akramin Zainuddin, N. Afiqah A. Aziz, Nor Nadirah Azman, Amelia Wong T Technology (General) 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. Science and Engineering Research Support Society 2016 Article PeerReviewed application/pdf en http://irep.iium.edu.my/53263/1/4.%20Journal_IJMUE.pdf Rashidan, M. Ariff and Mohd Mustafah, Yasir and Shafie, Amir Akramin and Zainuddin, N. Afiqah and A. Aziz, Nor Nadirah and Azman, Amelia Wong (2016) Moving object detection and classification using neuro-fuzzy approach. International Journal of Multimedia and Ubiquitous Engineering, 11 (4). pp. 253-266. ISSN 1975-0080 10.14257/ijmue.2016.11.4.26 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
T Technology (General) |
| spellingShingle |
T Technology (General) Rashidan, M. Ariff Mohd Mustafah, Yasir Shafie, Amir Akramin Zainuddin, N. Afiqah A. Aziz, Nor Nadirah Azman, Amelia Wong Moving object detection and classification using neuro-fuzzy approach |
| description |
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. |
| format |
Article |
| author |
Rashidan, M. Ariff Mohd Mustafah, Yasir Shafie, Amir Akramin Zainuddin, N. Afiqah A. Aziz, Nor Nadirah Azman, Amelia Wong |
| author_facet |
Rashidan, M. Ariff Mohd Mustafah, Yasir Shafie, Amir Akramin Zainuddin, N. Afiqah A. Aziz, Nor Nadirah Azman, Amelia Wong |
| author_sort |
Rashidan, M. Ariff |
| title |
Moving object detection and classification using neuro-fuzzy approach |
| title_short |
Moving object detection and classification using neuro-fuzzy approach |
| title_full |
Moving object detection and classification using neuro-fuzzy approach |
| title_fullStr |
Moving object detection and classification using neuro-fuzzy approach |
| title_full_unstemmed |
Moving object detection and classification using neuro-fuzzy approach |
| title_sort |
moving object detection and classification using neuro-fuzzy approach |
| publisher |
Science and Engineering Research Support Society |
| publishDate |
2016 |
| url |
http://irep.iium.edu.my/53263/ http://irep.iium.edu.my/53263/ http://irep.iium.edu.my/53263/1/4.%20Journal_IJMUE.pdf |
| first_indexed |
2023-09-18T21:15:24Z |
| last_indexed |
2023-09-18T21:15:24Z |
| _version_ |
1777411529090531328 |