Zainal Rasyid Mahayuddin

A review on gesture recognition using kinect

Kinect provides an interesting interaction between user and device with controller-free entertainment environment. It has been used widely in various fields; research, surveillance, medical, entertainment and etc. It is necessary for user to communicate and control a device in natural and efficient way in human-robot interaction based. Hand-controllers and electromechanical device have been used by humans to control robots or machines but there were some constraints in several factors of interaction. As of that, gesture recognition techniques were introduced to overcome those problems. Three sensors in Kinect; an infrared camera, an infrared laser projector and a color camera are used to track and recognize skeletal and human body. In this paper, analysis on gesture recognition in Kinect will be discussed. Several techniques will be compared and the best gesture recognition technique in term of accuracy and efficiency will be chosen in the end of study. At the end of the study, a technique will be proposed to increase the precision of the human gesture recognition using Kinect for better performance in human-robot interaction.

Adaptive Motion Pattern Analysis for Machine Vision Based Moving Detection from UAV Aerial Images

In order to detect moving object from UAV aerial images motion analysis has started to get attention in recent years where motion of the objects along with moving camera needs to be estimated and compensated by using detection algorithm. Moving object detection from UAV aerial images based on motion analysis involves modeling the pixel value changes over time. Moving object detection with moving cameras from UAV aerial images is still an unsolved issue due to not considering irregular motion of camera and improper estimation of noise, object motion changes and finally unfixed moving object direction. This paper presents a low complexity based motion analysis framework for moving object detection along with camera motion estimation by considering motion change of moving object and unfixed moving object direction. Based on the experimental results it is expected that proposed motion vector estimation performs well for both invariant motion and invariant moving object direction.

Moving Object Detection Using Dynamic Motion Modelling from UAV Aerial Images

Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

Real Time Vision Based Object Detection from UAV Aerial Images: A Conceptual Framework

In computer vision research, one of the capabilities of establishing an autonomous UAV is the detection of rigid and non-rigid object. Moving object detection with moving cameras from UAV aerial images is still an unsolved issue due to clutter and rural background contained in the images, even and uneven illumination changes, static and moving objects and motion of camera. This paper presents a conceptual framework for moving object detection with moving camera from UAV aerial images combined with the frame difference and segmentation approach together. Our focus is the human as rigid and vehicle as non rigid object detection where the camera can be mounted on the vehicle or other movable platform. It is expected that the proposed conceptual framework performs well under different situations for uneven environments.

Motion analysis for moving object detection from UAV aerial images: A review

Motion analysis for moving object from UAV aerial images is still an unsolved issue in computer vision research field due to fast abrupt motion of object and UAV, low resolution, noisy imagery, cluttered background, low contrast and small target size. The main reason for the inability to handle motion is the weakness of existing approaches for moving object detection. This paper presents critical analysis of the various methods used for motion analysis which states lack of relevancy with motion analysis along with some unsolved problems need to be solved for optimum performance of moving objects detection from UAV aerial images. The overall reviews proposed in this paper have been extensively studied in various research papers which can significantly contribute to computer vision research and can be potential for future development and direction for future research.

Moment Feature Based Fast Feature Extraction Algorithm for Moving Object Detection Using Aerial Images

Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerningmoving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents thecoherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.

Efficiency measurement of various denoise techniques for moving object detection using aerial images

Noise reduction from aerial images is considered an image restoration mechanism in which it attempts to recover image from a degraded image. Denoising image is considered as the key factor for minimizing the functionality for piecewise smooth image intensity. Efficiency of various denoising techniques depends on frame rate and finally computation time for overall object detection mechanism. Moving object detection is the first step of image denoising as well as object detection. This technique uses segmentation, motion detection and feature extraction technique. The main goal of this paper is to compare various noise reduction techniques incorporated with various moving object detection methods along with various features like edges and corners based detection. Experimentation was done based on two parameters frame rate and computation time which initiate to choose the best denoising method for moving object detection.

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

Face recognition has been used in various applications where personal identification is required. Other methods of persons identification and verification such as iris scan and finger print scan require high quality and costly equipment. The objective of this research is to present an extended principal component analysis model to recognize a person by comparing the characteristics of the face to those of new individuals for different dimension of face image. The main focus of this research is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background is constant. This research requires a normal camera giving a 2-D frontal image of the person that will be used for the process of the human face recognition. An Extended Principal Component Analysis EPCA technique has been used in the proposed model of face recognition. Based on the experimental results it is expected that proposed the EPCA performs well for different face images when a huge number of training images increases computation complexity in the database.