Pendeteksian dan Pengenalan Wajah Pada Foto Secara Real Time Dengan Haar Cascade dan Local Binary Pattern Histogram

Penelitian untuk mendeteksi dan mengenali wajah secara realtime sudah banyak dilakukan dengan beberapa algoritma, namun belum banyak yang menggunakan objek wajah pada foto secara realtime.  Dalam penelitian ini, penulis mengusulkan gabungan dua algoritma berupa algoritma Haar Cascade Classifier dan Local Binary Pattern Histogram (LBPH) untuk pengenalan wajah. Cara kerja algoritma tersebut dengan mendeteksi dan mengenali objek wajah pada foto secara realtime menggunakan web camera. Adapun metode yang digunakan adalah penyusunan dataset, proses training, proses deteksi serta proses pengenalan wajah. Pengujian dilakukan dengan menggunakan 240 dataset berupa citra wajah. Hasil dari penelitian ini menunjukkan bahwa pada jarak 0-40 cm, sistem mampu mendeteksi dan mengenali wajah secara maksimal. Namun, dengan jarak lebih dari 40 cm sistem belum mampu mendeteksi dan mengenali wajah secara maksimal

Converting a DJI Tello Quadcopter into a Face-follower Machine Using the Haar Cascade with PID Controller

Drones have been frequently used for photography in recent years at significantly cheaper rates. However, the most modern drones are exceedingly error-prone and require precise manual control to take high-quality photos or films. We suggest using the AI method of Haar cascades with a PID controller to give drones vision, enabling them to do autonomous tracking and detection. This project aims to improve photography fields. The proposed system tries to detect the face and track the person's movements. This system will help photographers and journalists upgrade their work, even if it is used in surveillance and the military. The algorithm's results show that the DJI Tello tiny drone's camera is capable of detecting and tracking faces. The micro drone was picked since it is lightweight and compact, making its use safe and enabling testing to take place inside. Additionally, the DJI Tello may be easily programmed using Python. The position of the drone is contrasted with the set point in the center of the image to identify errors, allowing control signals for calculating forward/backwards, right/left, and yaw movements. The proposed system results show that the drone can detect and track the face very well, and the PID values are stable

Penggunaan Metode Haar Cascade Classifier dan LBPH Untuk Pengenalan Wajah Secara Realtime

Pengenalan wajah manusia menjadi sebuah topik penelitian biometric yang cukup banyak diminatai karena pada wajah manusia terdapat banyak informasi terutama mengenai identitas seseorang. Setiap orang memiliki bentuk wajah yang berbeda yang dapat dilihat dari mata, hidung, telinga dan juga mulut. Pada penelitian ini penulis menggabungkan dua metode haar cascade classifoer dan LBPH untuk membuat sistemm yang dapat mengenali wajah seseorang. Metode haar casecade classifier digunakan untuk mendeteksi adanya wajah manusia sedangkan metode LBPH digunakan untuk mengenali wajah seseorang. Pada sistm ini terdapat beberapa proses untuk dapat mengenali wajah seseorang, yaitu: proses deteksi wajah, proses pengambilan dataset, proses pelatihan wajah dan proses pengenalan wajah. Proses pengambilan dataset dilakukan secara otomatis, saat sistem sudah mendeteksi adanya wajah manusia dan diambil sebanyak 40 foto untuk setiap satu wajah user. Sistem akan mencocokkan wajah yang terdeteksi dengan indentitas wajah yang telah dimasukkan ke dalam dataset. Selanjutnya sisstem akan mengenali wajah yang dideteksi dan menampikan nama sesuai dengan nomer user ID yang terdapat di dataset. Tampilan pengenalan wajah mnggunakan sistem realtime dimana nama yang ditampilkan sesuai dengan orang yang tepat berdiri didepan kamera laptop pada saat itu. Keberhasilan sistem ini sebesar 88,42%

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%

Trailer Reverse Assist. Optical Follow Me

Backing-up a trailer is a difficult task even for experienced users, and thus, solutions exist for assisting the steering of a vehicle-trailer system by just requiring the input of the desired trailer’s trajectory. Nevertheless, the problem is not entirely solved as the selected trajectory clearance while reversing a trailer is not completely available due to visibility obstructions, making reversing a trailer an unsafe maneuver. The objective of this work is to perform a proof-of-concept of a system which aids the user in the process of backing up a trailer through the desired trajectory where limited visibility is present. This was accomplished by developing an add-on feature capable of tracking a helping person. The new feature provides the required information so that existing compatible trailer reversing solutions can steer and accelerate the vehicle to follow the tracked person while also keeping a safe distance. Moreover, potential collisions are prevented by the addition of a close proximity object detection functionality. For this, a scaled prototype of the proposed system was developed by applying the “Vee Model” methodology where the requirements, architecture, solution design, implementation, and validation steps were followed. A successful proof-of-concept was accomplished after validating the capacity of the prototype to both identify and follow a person, while maintaining a safe distance, and to detect objects in the vehicle’s path. In addition, the documentation of the system’s design, development, and validation was achieved rendering the feature ready for full scale development. In conclusion, the “Trailer Reverse Assist - Optical Follow Me” system add-on can further assist in the process of backing-up a trailer safely in environments where the visibility is limited while also preventing collisions with nearby objects.ITESO, A. C.ContinentalConsejo Nacional de Ciencia y Tecnologí

Offline printed Arabic character recognition

Optical Character Recognition (OCR) shows great potential for rapid data entry, but has limited success when applied to the Arabic language. Normal OCR problems are compounded by the right-to-left nature of Arabic and because the script is largely connected. This research investigates current approaches to the Arabic character recognition problem and innovates a new approach. The main work involves a Haar-Cascade Classifier (HCC) approach modified for the first time for Arabic character recognition. This technique eliminates the problematic steps in the pre-processing and recognition phases in additional to the character segmentation stage. A classifier was produced for each of the 61 Arabic glyphs that exist after the removal of diacritical marks. These 61 classifiers were trained and tested on an average of about 2,000 images each. A Multi-Modal Arabic Corpus (MMAC) has also been developed to support this work. MMAC makes innovative use of the new concept of connected segments of Arabic words (PAWs) with and without diacritics marks. These new tokens have significance for linguistic as well as OCR research and applications and have been applied here in the post-processing phase. A complete Arabic OCR application has been developed to manipulate the scanned images and extract a list of detected words. It consists of the HCC to extract glyphs, systems for parsing and correcting these glyphs and the MMAC to apply linguistic constrains. The HCC produces a recognition rate for Arabic glyphs of 87%. MMAC is based on 6 million words, is published on the web and has been applied and validated both in research and commercial use