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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics
Mohd Asyraf Zulkifley
Universiti Kebangsaan Malaysia
Computer Science & IT Electrical & Electronics Engineering Engineering

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Deep-learning based single object tracker for night surveillance Zulaikha Kadim; Mohd Asyraf Zulkifley; Nabilah Hamzah
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (879.699 KB) | DOI: 10.11591/ijece.v10i4.pp3576-3587

Abstract

Tracking an object in night surveillance video is a challenging task as the quality of the captured image is normally poor with low brightness and contrast. The task becomes harder for a small object as fewer features are apparent. Traditional approach is based on improving the image quality before tracking is performed. In this paper, a single object tracking algorithm based on deep-learning approach is proposed to exploit its outstanding capability of modelling object’s appearance even during night. The algorithm uses pre-trained convolutional neural networks coupled with fully connected layers, which are trained online during the tracking so that it is able to cater for appearance changes as the object moves around. Various learning hyperparameters for the optimization function, learning rate and ratio of training samples are tested to find optimal setup for tracking in night scenarios. Fourteen night surveillance videos are collected for validation purpose, which are captured from three viewing angles. The results show that the best accuracy is obtained by using Adam optimizer with learning rate of 0.00075 and sampling ratio of 2:1 for positive and negative training data. This algorithm is suitable to be implemented in higher level surveillance applications such as abnormal behavioral recognition.
Moving object detection via TV-L1 optical flow in fall-down videos Nur Ayuni Mohamed; Mohd Asyraf Zulkifley
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (811.867 KB) | DOI: 10.11591/eei.v8i3.1346

Abstract

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524.
Moving object detection via TV-L1 optical flow in fall-down videos Nur Ayuni Mohamed; Mohd Asyraf Zulkifley
Bulletin of Electrical Engineering and Informatics Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (811.867 KB) | DOI: 10.11591/eei.v8i3.1346

Abstract

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524.
Co-Authors Nabilah Hamzah Nur Ayuni Mohamed Zulaikha Kadim