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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mohd Asyraf Zulkifley
Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia
Computer Science & IT Engineering

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Training configuration analysis of a convolutional neural network object tracker for night surveillance application Zulaikha Kadim; Mohd Asyraf Zulkifley; Nor Azwan Mohamed Kamari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (636.091 KB) | DOI: 10.11591/ijai.v9.i2.pp282-289

Abstract

Automated surveillance during the night is important as it is the period when crimes usually happened. By providing continuous monitoring, coupled with a real-time alert system, appropriate action can be taken immediately if a crime is detected. However, low lighting conditions during the night can degrade the quality of surveillance videos, where the captured images will have low contrast and less discriminative features. Consequently, these factors contribute to the problem of bad appearance representation of the object of interest in the tracking algorithm. Thus, a convolutional neural network-based object tracker for night surveillance is proposed by exploiting the deep feature strength in representing object features spatially and semantically. The proposed convolutional network consists of six layers that consist of three convolutional neural networks (CNN) and three fully connected (FC) layers. The network will be trained by using a binary classifier approach of objects and its background classes, which is updated on a fixed interval so that it fully encapsulates the changes in object appearance as it moves in the scene.  The algorithm has been tested with different sets of training data configurations to find the best optimum ones with regards to VOT2015 evaluation protocols, tested on 14-night surveillance videos. The results show that the configuration of a total of 250 training samples with a sample ratio of 4:1 between positive and negative data delivers the best performance for the sequence length of [1,550]. It can be inferred that more information on the object is required compared to the background, where the background might be homogeneous due to low lighting conditions. In conclusion, this algorithm is suitable to be implemented for night surveillance application.
Classification of tomato leaf diseases using MobileNet v2 Siti Zulaikha Muhammad Zaki; Mohd Asyraf Zulkifley; Marzuraikah Mohd Stofa; Nor Azwan Mohammed Kamari; Nur Ayuni Mohamed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (538.914 KB) | DOI: 10.11591/ijai.v9.i2.pp290-296

Abstract

Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.
Co-Authors Marzuraikah Mohd Stofa Nor Azwan Mohamed Kamari Nor Azwan Mohammed Kamari Nur Ayuni Mohamed Siti Zulaikha Muhammad Zaki Zulaikha Kadim