Identity Retention of Multiple Objects under Extreme Occlusion Scenarios using Feature Descriptors

Identity assignment and retention needs multiple object detection and tracking. It plays a vital role in behavior analysis and gait recognition. The objective of Multiple Object Tracking (MOT) is to detect, track and retain identities from an image sequence. An occlusion is a major resistance in identity retention. It is a challenging task to handle occlusion while tracking varying number of person in the complex scene using a monocular camera. In MOT, occlusion remains a challenging task in real world applications. This paper uses Gaussian Mixture Model (GMM) and Hungarian Assignment (HA) for person detection and tracking. We propose an identity retention algorithm using Rotation Scale and Translation (RST) invariant feature descriptors. In addition, a segmentation based optimum demerge handling algorithm is proposed to retain proper identities under occlusion. The proposed approach is evaluated on a standard surveillance dataset sequences and it achieves 97 % object detection accuracy and 85% tracking accuracy for PETS-S2.L1 sequence and 69.7% accuracy as well as 72.3% precision for Town Centre Sequence

IoT based Automated Plant Disease Classification using Support Vector Machine

Leaf - a significant part of the plant, produces foodusing the process called photosynthesis. Leaf disease can causedamage to the entire plant and eventually lowers crop production.Machine learning algorithm for classifying five types of diseases,such as Alternaria leaf diseases, Bacterial Blight, Gray Mildew,Leaf Curl and Myrothecium leaf diseases, is proposed in theproposed study. The classification of diseases needs front faceof leafs. This paper proposes an automated image acquisitionprocess using a USB camera interfaced with Raspberry PI SoC.The image is transmitted to host PC for classification of diseasesusing online web server. Pre-processing of the acquired image byhost PC to obtain full leaf, and later classification model basedon SVM is used to detect type diseases. Results were checkedwith a 97% accuracy for the collection of acquired images

Experimental Evaluation and Analysis of LED Illumination Source for Endoscopy Imaging

The minimally invasive surgery uses a small instrument with camera and light to fit the tiny cut in the skin. The selection of the light depends on the power and driving current of the circuit. It can also help in the standardization of the camera and capture the tissues' true-colour image. This paper presents the LED source analysis used in the clinical endoscopes for surgery and the human body's medical examination. Initially, a LED source selection mechanism generating intense illuminance in a visible band is proposed. A low-cost prototype model is developed to analyze the wavelength and illuminance of three different LEDs types. An effect on variation in LED illumination is investigated by changing the distance between the Borescope and LED source. True-colour image generation and tissue contrast are more important in medical diagnostics. Therefore, a sigmoid function improving the whole contrast ratio of the captured image in real-time is presented. The results of spectrum and wavelength for a current variation are presented. Type 3 LED produces higher illumination (i.e., 395 Klux) and peak wavelength (i.e., 622.05 nm) than other LEDs, while type-2 LED has better FWHM for the blue colour spectrum. The modification in the sigmoid function enhances the image with 34.25 peak PSNR producing a true-colour image

Smart android based home automation system using internet of things (IoT)

Recently, home automation system has getting significant attention because of the fast and advanced technology, making daily living more convenient. Almost everything has been digitalized and automated. The development of home automation will become easier and more popular because of the use of the Internet of Things (IoT). This paper described various interconnection systems of actuators, sensors to enable multiple home automation implementations. The system is known as HAS (Home automation system). It operates by connecting the robust Application Programming Interface (API), which is the key to a universal communication method. The HAS used devices, often implemented the actuators or sensors that have an upwards communication network followed by HAS (API). Most of the devices of the HAS (home automation system) used Raspberry Pi boards and ESP8285 chips. A smartphone application has been developed that allows users to control a wide range of home appliances and sensors from their smartphones. The application is user-friendly, adaptable, and beneficial for consumers and disabled people. It has the potential to be further extended via the use of various devices. The main objectives of this work are to make our home automation system, more secure and intelligent. HAS is a highly effective and efficient computational system that may be enhanced with a variety of devices and add-ons

Interactive Head Control of Embroidery Machine using Embedded Web Server

This paper proposes the design and implementation of prototype model to control embroidery machine using single board ARM processor. Wilcom is the software largely used for embroidery design. Designs prepared using Wilcom software are printed and supplied to the machine for further process. The proposed prototype model extracts the design data from this software by avoiding the printing and/or scanning of the user design and the design data are supplied to ARM based prototype embroidery machine for printing. In proposed model, the pencil is used as printing head and A4 size paper is used for the printing. Further to control the head, Use of web server design is also proposed using ARM processor

A novel approach for yoga pose estimation based on in-depth analysis of human body joint detection accuracy

Virtual motion and pose from images and video can be estimated by detecting body joints and their interconnection. The human body has diverse and complicated poses in yoga, making its classification challenging. This study estimates yoga poses from the images using a neural network. Five different yoga poses, viz. downdog, tree, plank, warrior2, and goddess in the form of RGB images are used as the target inputs. The BlazePose model was used to localize the body joints of the yoga poses. It detected a maximum of 33 body joints, referred to as keypoints, covering almost all the body parts. Keypoints achieved from the model are considered as predicted joint locations. True keypoints, as the ground truth body joint for individual yoga poses, are identified manually using the open source image annotation tool named Makesense AI. A detailed analysis of the body joint detection accuracy is proposed in the form of percentage of corrected keypoints (PCK) and percentage of detected joints (PDJ) for individual body parts and individual body joints, respectively. An algorithm is designed to measure PCK and PDJ in which the distance between the predicted joint location and true joint location is calculated. The experiment evaluation suggests that the adopted model obtained 93.9% PCK for the goddess pose. The maximum PCK achieved for the goddess pose, i.e., 93.9%, PDJ evaluation was carried out in the staggering mode where maximum PDJ is obtained as 90% to 100% for almost all the body joints