Object Distance Measurement System Using Monocular Camera on Vehicle

To support autonomous vehicles that are currently often studied by various parties, the authors propose to make a system of predicting the distance of objects using monocular cameras on vehicles. Distance prediction uses four methods and the input parameter was obtained from images processed with MobileNets SSD. Calculations using linear regression are the simplest calculations among the four methods but have an error of 1% with a standard deviation of 1.65 meters. While using the first method, the average error value is 9% with a standard deviation of 0.43 meters. By using the second calculation, the average error resulted in 6% with a standard deviation of 0.35 meters. The experimental method had an average error of 1% with a standard deviation of 0.26 meters, so the experimental method was used

Kombinasi Deteksi Objek, Pengenalan Wajah dan Perilaku Anomali menggunakan State Machine untuk Kamera Pengawas

ABSTRAKSaat ini sistem kamera pengawas mengandalkan manusia dalam melakukan penerjemahan pada rekaman gambar yang terjadi. Perkembangan computer vision, machine learning, dan pengolahan citra dapat dimanfaatkan untuk membantu peran manusia dalam melakukan pengawasan. Penelitian ini merancang sistem kerja kamera yang terdiri dari tiga modul yaitu deteksi objek, pengenalan wajah, dan perilaku anomali. Deteksi objek memakai HOG-SVM, pengenalan wajah menggunakan CNN dengan arsitektur VGG-16 memanfaatkan transfer learning, dan perilaku anomali memakai spatiotemporal autoencoder berdasarkan threshold. Ketiga modul tersebut diuji menggunakan metrik akurasi, presisi, recall, dan f1-score. Ketiga modul diintegrasikan dengan state machine menjadi satu kesatuan sistem. Kinerja modul memiliki akurasi 88% untuk deteksi objek, 98% untuk pengenalan wajah, dan 78% untuk perilaku anomali. Hasil tampilan riil dapat diakses secara sederhana dan nirkabel melalui web.Kata kunci: HOG-SVM, CNN, VGG-16, spatiotemporal autoencoder, state machineABSTRACTNowadays, the surveillance camera system relies on human to interpret the recorded images. Computer vision, machine learning, and image processing can be utilized to assist the human role in supervising. This study designed a camera work system consisting of three main modules, namely object detection, face recognition, and anomaly behavior. Object detection used the HOG-SVM combination. Facial recognition used CNN with the VGG-16 architecture that utilized transfer learning. Anomalous behavior used spatiotemporal autoencoder based on threshold. Modules are tested using the metrics of accuracy, precision, recall, and f1-score. The three modules are integrated using a state machine into one system. The performance of the module had 88% accuracy for object detection, 98% for facial recognition, and 78% for anomalous behavior. Real time video recording can be accessed wireless via web-based.Keywords: HOG-SVM, CNN, VGG-16, spatiotemporal autoencoder, state machin

Deep features fusion for KCF-based moving object tracking

Abstract Real-time object tracking and occlusion handling are critical research areas in computer vision and machine learning. Developing an efficient and accurate object-tracking method that can operate in real-time while handling occlusion is essential for various applications, including surveillance, autonomous driving, and robotics. However, relying solely on a single hand-crafted feature results in less robust tracking. As a hand-crafted feature extraction technique, HOG effectively detects edges and contours, which is essential in localizing objects in images. However, it does not capture fine details in object appearance and is sensitive to changes in lighting conditions. On the other hand, the grayscale feature has computational efficiency and robustness to changes in lighting conditions. The deep feature can extract features that express the image in more detail and discriminate between different objects. By fusing different features, the tracking method can overcome the limitations of individual features and capture a complete representation of the object. The deep features can be generated with transfer learning networks. However, selecting the right network is difficult, even in real-time applications. This study integrated the deep feature architecture and hand-crafted features HOG and grayscale in the KCF method to solve this problem. The object images were obtained through at least three convolution blocks of transfer learning architecture, such as Xception, DenseNet, VGG16, and MobileNet. Once the deep feature was extracted, the HOG and grayscale features were computed and combined into a single stack. In the KCF method, the stacked features acquired the actual object location by conveying a maximum response. The result shows that this proposed method, especially in the combination of Xception, grayscale, and HOG features, can be implemented in real-time applications with a small center location error