Browsing by Author "Mahayuddin, Zainal Rasyid"

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Moving Object Detection Using Semantic Convolutional Features
(2022-12-31) Mahayuddin, Zainal Rasyid; Saif, A
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Moving object detection from aerial images remains an unsolved problem in computer vision research domain. Detection results are not precise due to blurry aerial images, thin edges and noise. Various methods were previously proposed for moving object detection which could not provide robust results due many challenges, i.e., noise, motion detection, lack of appropriate features, lack of effective classification approach, complex background and variations in illumination. This research proposes an efficient method for moving object detection using convolutional semantic features from VGG-16 to use motion patterns to facilitate detection in each frame and provides smaller area as region of interest. Proposed method reduces probability motion intensity information getting lost in case of same coloured object in the background and thus minimizes background complexity. After that, proposed method performs semantic features distance measurement to calculate linear distances in each frame. In this context, if there is any frame loss due to noise or illumination variation, proposed method uses Kalman filter to process that frame by illuminating noise. Finally, decision for final moving detection is determined using random forest classifier from semantic convolutional feature vector by generating a set of probabilities for each class. Experimental results show that the proposed method can detect moving objects efficiently, which in turn will decrease the operating time and increase the detection rate compared to previous research methods.
Stereo Vision Based Localization of Handheld Controller in Virtual Reality for 3D Painting Using Inertial System
(2023-06) Saif, A. F.M.Saifuddin; Mahayuddin, Zainal Rasyid
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Google Tilt Brush is expensive for virtual drawing which needs further improvement on the functionalities of mechanisms rather than implementation expects addressed in this research. Several issues are addressed by this research in this context, i.e., noise removal from sensor data, double integration-based drift issues and cost. Recently, available smart phones do not have the ability to perform drawing within artificial settings handling cardboard and daydream of google without purchasing Oculus Rift and HTC Vive (Virtual Reality Headset) because of expensiveness for large number of users. In addition, various extrinsic hardwares, i.e., satellite localization hardware and ultrasonic localization applications are not used for drawing in virtual reality. Proposed methodology implemented extended Kalman filter and Butterworth filter to perform positioning using six degree of freedom using Microelectromechanical Applications (MEMS) software data. A stereo visual method using Simultaneous Localization and Mapping (SLAM) is used to estimate the measurement for positioning implicating mobile phone (i.e., android platform) for the hardware system to estimate drift. This research implemented Google Virtual Reality application settings Kit with Unity3D engine. Experimentation validation states that proposed method can perform painting using virtual reality hardware integrated with controller software implicating smartphone mobile without using extrinsic controller device, i.e., Oculus Rift and HTC Vive with satisfactory accuracy.
Vision based 3D Object Detection using Deep Learning: Methods with Challenges and Applications towards Future Directions
(2022-11) Saif, F. M.Saifuddin; Mahayuddin, Zainal Rasyid
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
For autonomous intelligent systems, 3D object detection can act as a basis for decision making by providing information such as object’s size, position and direction to perceive information about surrounding environment. Successful application using robust 3D object detection can hugely impact robotic industry, augmented and virtual reality sectors in the context of Fourth Industrial Revolution (IR4.0). Recently, deep learning has become potential approach for 3D object detection to learn powerful semantic object features for various tasks, i.e., depth map construction, segmentation and classification. As a result, exponential development in the growth of potential methods is observed in recent years. Although, good number of potential efforts have been made to address 3D object detection, a depth and critical review from different viewpoints is still lacking. As a result, comparison among various methods remains challenging which is important to select method for particular application. Based on strong heterogeneity in previous methods, this research aims to alleviate, analyze and systematize related existing research based on challenges and methodologies from different viewpoints to guide future development and evaluation by bridging the gaps using various sensors, i.e., cameras, LiDAR and Pseudo-LiDAR. At first, this research illustrates critical analysis on existing sophisticated methods by identifying six significant key areas based on current scenarios, challenges, and significant problems to be addressed for solution. Next, this research presents strict comprehensive analysis for validating 3D object detection methods based on eight authoritative 3D detection benchmark datasets depending on the size of the datasets and eight validation matrices. Finally, valuable insights of existing challenges are presented for future directions. Overall extensive review proposed in this research can contribute significantly to embark further investigation in multimodal 3D object detection.