Assoc. Prof. Dr. Siti Norul Huda bt Sheikh Abdullah
Associate Professor
Center for Cyber Security
Faculty of Information Science and Technology
Universiti Kebangsaan Malaysia
snhsabdullah@ukm.edu.my

NO	NAME	INSTITUTION	FACULTY/SCHOOL/ CENTRE/UNIT
1	Prof. Dr. Khairuddin Omar	UKM	Faculty of Information Science and Technology
2	Assoc. Prof. Dr. Md Jan Bin Nordin	UKM	Faculty of Information Science and Technology
3	Prof. Dr. Chan Chee Seng	UM	Faculty of Information Science and Technology
4	Assoc. Prof. Ts. Dr. Azizi Bin Abdullah	UKM	Faculty of Information Science and Technology
5	Assoc. Prof. Dr. Shahnorbanun Sahran	UKM	Faculty of Information Science and Technology
6	Dr. Afzan Binti Adam	UKM	Faculty of Information Science and Technology
7	Mr.Shariffpudin Basiron
8	Prof. Dr. Masri Binti Ayob	UKM	Faculty of Information Science and Technology

2 years (4 October 2016 - 3 October 2018)

In Malaysia, vehicle recognition systems (VRS) are rapidly growing due to SPS immense potential to be commercialized. Furthermore, it can generate a new revenue market that can benefit the society and country. VRS systems have been applied in many areas such as to identify vehicle identities for the law enforcement by authorities and electronic toll collection by highway agencies. There exist plenty of VRS applications such as vehicle plate recognition and counting. In the vehicle plate recognition system there exists a process for license plate localization. The process is often faced with many obstacles when dealing, such as with illumination. Next, the vehicle counting may face the problem of reference lines which are needed to count the vehicle accurately. One possible solution to the problem is to use edge vertical projection for plate localization and object contour for vehicle counting. Though the method is claimed to be robust to illumination, it tends to create false edges that can hinder the recognition performance. Besides, the existing methods are sensitive to noises. Thus, this research aims to study a license plate localization method that is based on edge analysis. The proposed localization method consists of four main steps, namely pre-processing, rectangular blob searching, analysis and the vertical rectangular blobs projection. The proposed method is then tested on the European number plate datasets i.e. Baza Slika which contains about 167 vehicle images and Ondrej which contains about 97 vehicle images. The experimental results show that the proposed method outperforms the Ondrej method by obtaining an accuracy of 95% on the Baza Slika dataset and slightly lower by an accuracy of 91% on the Ondrej dataset. Then, the proposed method was tested on the Malaysia vehicle dataset namely Tol Sungai Long dataset which contains about 584 images of different illumination conditions, i.e. 297 images in the morning, 140 images in the afternoon and 147 images in the night. The proposed method outperforms other approaches with accuracy of 91.24%, 93.57% and 75.51% in the morning, evening and night respectively.

1. To implement a low cost vehicle classification based on visual input from a real time application.
2. To implement intelligent license plate recognition robust to color variations.
3. To test integration of license plate recognition and vehicle classification for business and community surveillance purposes.

1. Intellectual Property (IP) :
   

   Proposed	Achieve
   1	1

   
   
   • Copyright LY2018003381
2. Product/Prototype:
   • MyVis@Surada
   • EzCam
3. Publication:
   3 Conference Paper - National
   • License Plate Localization based on Kapur Optimal Multilevel Threshold (2017) – SCOPUS
   • Contour Based Tracking for Driveway Entrance Counting System (2019) – SCOPUS
   • Advances in Visual Informatics (2017) – Springer

• JomLaunch v6.0 (2018) – Special Awards
• Symposium on Control System and Signal Processing (2019) – Special Awards

EzCam 2.0 and 3.0 can support the needs of an intelligent transportation system at the edge IR4.0 to monitor safe cities. This is aligned with the 172 Act and Akta Perancangan Bandar dan Desa (Pindaan) 2017 [A1522 Act] under the Ministry of PlanMalaysia.