Automatic Human Joint Detection Using Microsoft Kinect

Automatic human joint detection has been used in many application nowadays. In this paper, we propose an approach to detect full body human joint method using depth and color image. The proposed solution is divided into 3 stage, which is image preprocess stage, distance transform stage, and anthropometric constraint analysis stage. The output of our solution is a stickman model with the same pose as in the given input image. Our implementation is done by using a Microsoft Kinect RGB and depth camera with 480x640 image resolution. The performance of this solution is demonstrated on several human posture

Pengujian Smart Doorbell Menggunakan Kamera dan Metode Haar-casscade

Pada umumnya kedatangan seorang tamu, pemilik rumah mengetahui dari suara bel listrik konvensional yang ditekan oleh tamu namun ketika pemilik rumah sedang tidak berada didalam rumah, pemilik rumah tidak mengetahui keberadaan tamu yang datang. Berdasarkan permasalahan tersebut, Smart Doorbell berbasis Internet of Things (IoT) dirancang untuk mengetahui datangnya tamu melalui deteksi OpenCV dengan metode Haar-cascade yang memberikan notifikasi pada smartphone melalui email dan notifikasi suara modul buzzer didalam rumah. Dengan adanya Smart Doorbell berbasis IoT pemilik rumah dapat mengetahui informasi kedatangan tamu walaupun pemilik rumah tidak berada di rumah. Hasil dari penelitian ini menunjukan bahwa klasifikasi menggunakan upperbody recognition lebih baik dibandingkan dengan face recognition dengan nilai rata-rata selisih waktu terdeteksi 6,05 detik pada delay 30 detik dan 6,31 detik pada delay 60 detik dan akurasi sebesar 95%

Automatic Human Joint Detection Using Microsoft Kinect

Automatic human joint detection has been used in many application nowadays. In this paper, we propose an approach to detect full body human joint method using depth and color image. The proposed solution is divided into 3 stage, which is image preprocess stage, distance transform stage, and anthropometric constraint analysis stage. The output of our solution is a stickman model with the same pose as in the given input image. Our implementation is done by using a Microsoft Kinect RGB and depth camera with 480x640 image resolution. The performance of this solution is demonstrated on several human posture

PERANCANGAN DAN IMPLEMENTASI PENGOLAHAN GAMBAR DETEKSI MANUSIA SEBAGAI MONITORING BENCANA BANJIR DENGAN BERBASIS RASPBERRY PI

Pemanfaatan aeromodelling dewasa ini semakin beragam. Salah satu pemanfaatan aeromodelling adalah monitoring bencana. Monitoring bencana bertujuan untuk mempermudah dalam evakuasi korban. Dalam proses pencarian korban diperlukan sebuah metode yang dapat mengidentifikasi manusia. Oleh karena itu diimplementasikan pengolahan citra HaarCascade sebagai metode pendeteksian obyek manusia. pengolahan pendeteksian dibuat menggunakan mini computer Raspberry pi yang diaplikasikan dalam quadcopter, dan menstreaming kan proses video ke groundstation menggunakan mjpg streamer. Hasil dari penelitian tugas akhir ini adalah didapat akurasi tertinggi untuk kasus-kasus manusia sebesar 75% yaitu ketika manusia berhimpit dengan posisi menghadap depan kamera dan jarak paling optimal dalam mendeteksi yaitu pada jarak 3 - 4 meter dengan presentase 80% - 90%. Sedangkan untuk performansi kecepatan pengiriman didapat hingga 7.5–8.5 fps dengan resolusi paling optimal 320x240. Kata Kunci : Human Detection, Computer Vision, Haa

Geometrical-based approach for robust human image detection

In recent years, object detection and classification has been gaining more attention, thus, there are several human object detection algorithms being used to locate and recognize human objects in images. The research of image processing and analyzing based on human shape is one of the hot topic due to the wide applicability in real applications. In this paper, we present a new object classification approach. The new approach will use a simple and robust geometrical model to classify the detected object as human or non-human in the images. In the proposed approach, the object is detected. Then the detected object under different conditions can be accurately classified (i.e. human, non-human) by combining the features that are extracted from the upper portion of the contour and the proposed geometrical model parameters. A software-based simulation using Matlab was performed using INRIA dataset and the obtained results are validated by comparing with five state-of-art approaches in literature and some of the machine learning approaches such as artificial neural networks (ANN), support vector machine (SVM), and random forest (RF). The experimental results show that the proposed object classification approach is efficient and achieved a comparable accuracy to other machine learning approaches and other state-of-art approaches. Keywords: Human classification, Geometrical model, INRIA, Machine learning, SVM, ANN, Random forest

CAMBADA@Home: deteção e seguimento de humanos

Mestrado em Engenharia Electrónica e TelecomunicaçõesEste trabalho apresenta uma abordagem ao problema da deteção e seguimento de humanos, usando uma câmara RGB-D. Existem soluções propostas para este tipo de problema, no entanto, algumas são baseadas em técnicas de extração de fundo ou outras e, como tal, necessitam que a câmara se encontre numa posição estacionária. Com o sistema proposto, a deteção e seguimento podem ser desempenhadas enquanto a câmara se move, em tempo real. O objetivo deste projeto é a implementação de um sistema de deteção e seguimento de pessoas para o robô de serviço CAMBADA@Home, permitindo assim o desenvolvimento de futuras aplicações na área da interação humano-robô. O sistema aqui descrito permite realizar deteção, classificação e monitorização de múltiplas pessoas. Na primeira etapa, regiões de interesse (ROIs) são segmentadas através da análise do histograma da imagem de profundidade seguido da utilização de um algoritmo de preenchimento. Na etapa seguinte, cada região é classificada como humana ou não-humana através de uma técnica de correspondência de modelos, baseada no algoritmo de descida de gradiantes RPROP, com suporte para múltiplos modelos. A terceira e última etapa permite a monitorização de várias pessoas, através de um método de atribuição de identificadores únicos baseado em comparação de histogramas, assim como estimação de pose e localização. Os resultados obtidos em ambiente não controlado são encorajadores, com altas taxas de deteção, e, em geral, os algoritmos de estimação de pose e localização são executados como esperado. Para além disto, o projeto CAMBADA@Home foi premiado com o primeiro lugar no Desafio Free Bots, que teve lugar durante o campeonato nacional de robótica, Robótica 2013, onde o robô provou ser capaz de executar rondas autónomas num ambiente desconhecido enquanto detetava e monitorizava pessoas com as quais se cruzava.This work presents an approach to the people detection and tracking problem, using an RGB-D camera. While there are already solutions for this problem, some are based on background extraction techniques or other, which require the camera to be in a stationary position. With the proposed method, detection and tracking can be performed while the camera is moving, in real time. The aim of this project is the implementation of a people detection and tracking system for the CAMBADA@Home service robot, enabling the development of further human-robot interaction applications. The system here described enables object detection, classi cation and multiple person tracking. In the rst stage, regions of interest (ROIs) are segmented through the analysis of the depth image histogram and using a ood ll algorithm. On the next stage, each region is classi ed as human or not-human using a template matching technique, based on the RPROP gradient descent algorithm, with support for multiple templates. The third and last stage enables the tracking for multiple persons, using a unique identi cation assignment method based on histogram comparison, as well as pose and location estimation. The results obtained in unconstrained environments are encouraging, with high detection rates, and, in general, the algorithms for pose and location estimation perform as expected. Furthermore the CAMBADA@Home project has been awarded with the rst place in the Free Bots Challenge, which took place on the Rob otica 2013 robotics national championship, where the robot was proven to be capable of performing autonomous tours in an unknown environment while at the same time detecting and tracking people it came across

Designing a Contactless, AI System to Measure the Human Body using a Single Camera for the Clothing and Fashion Industry

Using a single RGB camera to obtain accurate body dimensions rather than measuring these manually or via more complex multi-camera or more expensive 3D scanners, has a high application potential for the apparel industry. In this thesis, a system that estimates upper human body measurements using a set of computer vision and machine learning techniques. The main steps involve: (1) using a portable camera; (2) improving image quality; (3) isolating the human body from the surrounding environment; (4) performing a calibration step; (5) extracting body features from the image; (6) indicating markers on the image; (7) producing refined final results. In this research, a unique geometric shape is favored, namely the ellipse, to approximate human body main cross sections. We focus on the upper body horizontal slices (i.e. from head to hips) which, we show, can be well represented by varying an ellipse’s eccentricity, this per individual. Then, evaluating each fitted ellipse’s perimeter allows us to obtain better results than the current state-of-the-art for use in the fashion and online retail industry. In our study, I selected a set of two equations, out of many other possible choices, to best estimate upper human body horizontal cross sections via perimeters of fitted ellipses. In this study, I experimented with the system on a diverse sample of 78 participants. The results for the upper human body measurements in comparison to the traditional manual method of tape measurements, when used as a reference, show ±1cm average differences, sufficient for many applications, including online retail