REGION CONVOLUTIONAL NEURAL NETWORK SIAMESE UNTUK DETEKSI OBJEK REFERENSI PADA VIDEO REKAMAN CCTV

Dalam mendeteksi sebuah objek terdapat kasus di mana akan sulit untuk melakukan pendeteksian pada video rekaman CCTV, terlebih perangkat yang dipasang pada tempat yang akan merekam banyak objek yang berbeda dari waktu ke waktu. Jika pendeteksian objek pada CCTV dilakukan secara manual menggunakan tenaga manusia, akan memerlukan waktu yang relatif panjang mengingat memantau setiap layar kamera bukanlah pekerjaan yang mudah. Sehingga pendekatan yang dapat digunakan ialah pendekatan objek referensi. Pada penelitian ini akan digunakan penggabungan dari metode RCNN dengan Siamese, di mana pendeteksian wilayah proposal akan diganti dengan metode RPN dan klasifikasi dengan metode Siamese untuk mencari nilai kesamaan antar dua objek. Terdapat 4 objek yang akan dideteksi. Selain itu, akan dibuat dua buah model dengan arsitektur yang berbeda, di mana salah satu dari model tersebut ditambahkan metode perhitungan euclidean distance dan diuji untuk dilihat hasil mAP serta akurasi dari masing-masing model. Hal ini bertujuan untuk mengetahui kinerja model dari penggabungan metode tersebut. Dari penelitian yang telah dilakukan, model yang menggunakan euclidean distance mendapatkan hasil lebih tinggi dengan mAP sekitar 2% hingga 56% dan akurasi sekitar 4% hingga 27% bergantung pada proses awal pendeteksian, perubahan yang signifikan terhadap bentuk objek yang ditangkap oleh kamera dibandingkan dengan gambar objek target, dan pencahayaan pada video rekaman CCTV. In object detection, there are cases where will be difficult to detect from CCTV video footage, especially devices installed in places that will record many different objects from time to time. If object detection on CCTV is done manually using human operator, it will take a relatively long time considering that monitoring each camera screen is not an easy job. The approach that can be used is the object reference. In this study, the combination of RCNN method with Siamese will be used, where the detection of the proposal region will be replaced by the RPN method and classification with the Siamese method to find the similarity value between two objects. There are four objects to be detected. In addition, which two models with different architectures will be made, where one of the models is added with the Euclidean distance calculation method and tested to see the mAP results and the accuracy of each model. It aims to determine the performance of the model from the combination of these methods. The results shown the model that uses the Euclidean distance gets a higher with mAP of around 2% to 56% and accuracy of around 4% to 27%, depend on the initial detection process, a significant change in the shape of the object captured by the camera compared to the image of the target object, and illumination on CCTV video footage

CNN Feature Map Interpretation and Key-Point Detection Using Statistics of Activation Layers

Convolutional Neural Networks (CNNs) have evolved to be very accurate for the classification of image objects from a single image or frames in video. A major function in a CNN model is the extraction and encoding of features from training or ground truth images, and simple CNN models are trained to identify a dominant object in an image from the feature encodings. More complex models such as RCNN and others can identify and locate multiple objects in an image. Feature Maps from trained CNNs contain useful information beyond the encoding for classification or detection. By examining the maximum activation values and statistics from early layer feature maps it is possible to identify key points of objects, including location, particularly object types that were included in the original training data set. Methods are introduced that leverage the key points extracted from these early layers to isolate objects for more accurate classification and detection, using simpler networks compared to more complex, integrated networks. An examination of the feature extraction process will provide insight into the information that is available in the various feature map layers of a CNN. While a basic CNN model does not explicitly create instances of visual or other types of information expression, it is possible to examine the Feature Map layers and create a framework for interpreting these layers. This can be valuable in a variety of different goals such object location and size, feature statistics, and redundancy analysis. In this thesis we examine in detail the interpretation of Feature Maps in CNN models, and develop a method for extracting information from trained convolutional layers to locate objects belonging to a pre-trained image data set. A major contribution of this work is the analysis of statistical characteristics of early layer feature maps and development of a method of identifying key-points of objects without the benefit of information from deeper layers. A second contribution is analysis of the accuracy of the selections as key-points of objects present in the image. A third contribution is the clustering of key-points to form partitions for cropping the original image and computing detection using the simple CNN model. This key-point detection method has the potential to greatly improve the classification capability of simple CNNs by making it possible to identify multiple objects in a complex input image, with a modest computation cost, and also provide localization information

Navigation and control of a quadcopter for real-time image processing applications

Dissertation (MEng (Computer Engineering))--University of Pretoria, 2022.In several quadcopter-related research work, the research mainly focuses on the image processing application and generally provides an inadequate description of the navigation and control of the quadcopter. While image processing is an important and complex component, it can prove to be unusable if the navigation and control of the quadcopter are of inferior quality. This article focuses on providing a primer to design the navigation and control for a quadcopter that can be used for real-time image processing applications. A variate of PID and PD controllers, along with Kalman filters, were used to develop the flight controller. Earplugs were installed to reduce the vibrations that were transmitted from the frame to the flight controller board, which was particularly helpful, as the accelerometer can be affected by vibrations. Furthermore, three-blade propellers were tested, and it was found that the three-blade propellers provided better overall stability; however, it experienced a slightly longer response time. It can be noted that the three-blade propellers perform better for image processing applications. A person tracking application was used to demonstrate the navigation and control of the quadcopter for real-time image processing applications. In conclusion the flight controller developed can be used for real-time image processing applications.University of PretoriaSAAB Grintek DefenceElectrical, Electronic and Computer EngineeringMEng (Computer Engineering)Unrestricte