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"Simulasi Kompresi Sinyal Video Digital ke Sinyal Audio merupakan suatu sistem kompresi data video digital dengan bitrate yang tinggi menjadi data video digital dengan bitrate yang rendah agar dapat dilewatkan pada transmisi sinyal audio yang bandwidthnya sebesar 64 Kbps. Sistem ini menggunakan kompresi Windows Media Video 9 karena dianggap mampu melakukan kompresi data video digital hingga 64 Kbps. Dengan mode encoding Constant Bit Rate (CBR), bitrate data video akan tetap konstan atau mendekati target bitrate yang sudah diatur sebelumnya.

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Simulation of Digital Video Compression Signal to Audio Signal is a digital video data compression system with a high bitrate digital video data to a lower bitrate in order to pass the audio signal transmission bandwidth of 64 Kbps. The system uses Windows Media Video 9 compression because it is able to perform data compression digital video of up to 64 Kbps. With Constant Bit Rate encoding mode (CBR), bitrate video data will remain constant or close to the target bitrate is prearranged."

"Kecerdasan buatan (artificial intelligence, AI) merupakan teknologi yang sedang berkembang dengan cepat pada masa ini. Adanya teknologi AI membuat banyak permasalahan sederhana dan kompleks dapat diatasi dengan program komputer. Salah satu penerapan dari teknologi AI yang memiliki perkembangan yang besar adalah pada computer vision, yang mana dapat dibuat program yang dapat mendeteksi dan mengklasifikasi objek pada suatu gambar. Pada bidang ini, computer vision dapat digunakan untuk mendeteksi rokok. Algoritma dapat dibuat untuk mengetahui jika ada objek rokok dan lokasi dari rokok tersebut pada gambar. Hal ini dapat berguna untuk menyensor rokok pada media video yang dikonsumsi oleh anak-anak. Pada media video, biasanya sensor dilakukan dengan cara manual dan dengan bantuan tracking. Cara ini dapat melelahkan karena walaupun dengan tracking, harus ada orang sebagai pendeteksi yang menunjukkan lokasi objek rokok secara berkala. Terdapat banyak arsitektur dan model algoritma untuk deteksi objek, salah satunya adalah YOLOv8 (You Only Look Once version 8). YOLOv8 adalah versi terbaru dari algoritma YOLO, yang mana merupakan salah satu algoritma state-of-the-art dalam deteksi objek. YOLO merupakan model dari Convolutional Neural Network (CNN) yang melakukan deteksi dengan konsep single stage detector, yaitu algoritma ini melakukan deteksi objek dengan menggunakan keseluruhan gambar sekaligus untuk menjadi masukan input neural network-nya. Cara ini membuat YOLO memiliki tingkat kecepatan yang tinggi mendekati real-time. Selain deteksi objek, diterapkan juga algoritma tracking yang berfungsi untuk menandai pergerakan objek rokok pada video. Sehingga objek rokok akan tetap disensor walaupun terjadi perubahan cahaya, terhalang objek lain, dan gangguan visual lainnya pada video. Algoritma tracking yang digunakan pada penelitian ini adalah ByteTrack. ByteTrack adalah algoritma tracking yang menggunakan komputasi yang minim karena dapat melakukan tracking dengan hanya memproses lokasi bounding box tiap frame video. Perbedaan algoritma ini dibandingkan yang lain adalah ByteTrack akan memanfaatkan semua hasil deteksi objek walaupun terdapat nilai confidence yang kecil. Pada penelitian ini didapatkan model training terbaik dari YOLOv8 dengan nilai presisi sebesar 86,5%, nilai recall sebesar 86,1%, nilai mAP 50 sebesar 88,1%, dan nilai mAP 50:95 sebesar 58,3%. Lalu pada konfigurasi confidence ByteTrack didapatkan hasil terbaik dengan pada confidence tahap pertama sebesar 0,247 dan tahap kedua sebesar 0,01. Hasil tracking ini mendapatkan nilai presisi sebesar 62,3%, nilai recall sebesar 62,7%, nilai akurasi sebesar 45,5%, dan nilai F1 sebesar 62,5%.

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Artificial intelligence (AI) is a technology that is developing rapidly and popular in this era. AI technology creates the possibility to solve and overcome many simple complex problems. One example of the application of AI technology that has great development is computer vision, which is a concept that can make a computer program to detect and classify objects in an image.  Using computer vision, this technology can be used to detect cigarette. From image or video media, the algorithm can check if there is any cigarette and then locate the object in the image. This is useful to censor cigarette from media that consumed by children. On video medium, censorship usually done manually with the help of object tracking. This method can be tiring because even if object tracking is used, there must be a person as a detector that locate the cigarette every few frames. There are many architectures and models for object detection, YOLOv8 (You Only Look Once version 8) is one of them. YOLOv8 is the latest version of YOLO algorithm. YOLOv8 is considered as one of the state-of-the-art algorithm for object detection.  YOLO model is based from Convolutional Neural Network (CNN). The concept of this algorithm to detect object is called single stage detector, which means that it takes the whole image as input for its neural network thus only single image process needed. This concept makes YOLO fast to detect objects. Object tracking algorithm is also used to keep track detected cigarette even if there is a change in light, occlusion from other object, and other visual changes in the video. ByteTrack is used for the tracking algorithm in this study. ByteTrack works by processing bounding box location of each frame in video, making it use little computation. The main difference of this algorithm is that it process all bounding boxes from the object detection, including detected object with low confidence score. In this study, the YOLOv8 model managed to obtain the best performance with precision value of 86.5%, recall value of 86.1%, mAP 50 value of 88.1%, and mAp 50:95 value of 58.3%. For the confidence configuration of ByteTrack, best performance is achieved with 0.247 confidence score for the first association and 0.01 confidence score for the second association. The result of this configuration is a precision value of 62.5%, a recall value of 62.7%, an accuracy value of 45.5%, and a F1 score of 62.5%."

"Live streaming merupakan salah satu metode transmisi data yang digunakan untuk menonton sebuah video. Transmisi live streaming akan mengirimkan file video secara sedikit demi sedikit kepada penonton secara langsung sehingga penonton dapat menyaksikan video yang ditransmisikan secara langsung atau disebut dengan real-time tanpa perlu mengunduh video sehingga video dapat disiarkan secara langsung. Pada saat ini live streaming merupakan salah satu bentuk konten video yang banyak diminati pada semua kalangan dan bentuk seperti siaran TV, streaming video game, video media sosial, hingga pengiklanan produk penjualan. Pada penelitian ini sistem pendeteksi komponen komputer sebagai produk penjualan video live streaming dilakukan dengan metode Object Detection dengan menggunakan model YOLOv7 yang merupakan model dari computer vision untuk dapat melakukan object detection secara real-time. Penelitian akan membandingkan variasi model dari YOLOv7 untuk menemukan varian yang paling sesuai untuk digunakan sebagai model pendeteksian pada video live streaming. Penelitian ini juga akan mencaritahu pengaruh dari resolusi dan juga jarak perekaman video terhadap tingkat akurasi model dengan melakukan ujicoba model terhadap video simulasi live streaming. Hasil percobaan membuktikan bahwa varian YOLOv7-Tiny menjadi yang paling sesuai untuk diimplementasikan kedalam video live streaming dikarenakan kecepatan pendeteksian yang lebih cepat dengan kecepatan 4.5 kali lebih cepat dari varian YOLOv7 dan sekitar 21.7 kali lebih cepat dari varian YOLOv7-X. Pengaruh jarak juga terbukti dengan menurunnya nilai mAP 0.5 yang dihasilkan model ketika jarak yang digunakan semakin besar dan perubahan resolusi juga terbukti berpengaruh terhadap viii kemampuan deteksi model dengan jumlah objek dan juga beban yang dihasilkan semakin besar seiring dengan meningkatnya resolusi yang digunakan.

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Live Streaming is one of the data transmission methods used to watch a video. Live Streaming transmission will send video files bit by bit to the viewer directly so that the viewer can watch videos that are transmitted directly or referred to as real time without the need to download the video so that the video can be broadcast live. At this time Live Streaming is a form of video content transmission method that is in great demand among all groups and forms such as TV broadcasts, video game streaming, social media videos, and also product advertising. In this study, the detection system for computer component as selling product on video live streaming was carried out with Object Detection method using YOLOv7 model, which is an computer vision model capable for object detection on real-time video. This study will also find out the effect of resolution and video recording distance on the accuracy of the model by testing the model on live streaming video simulations. The experimental results prove that the YOLOv7-Tiny variant is the most suitable to be implemented into live streaming video due to faster detection speed with a speed 4.5 times faster than the YOLOv7 variant and around 21.7 times faster than the YOLOv7-X variant. The effect of distance is also evident by the decreasing of mAP 0.5 value which is produced by the model when the distance used is greater and changes in resolution are also proven to affect the detection ability of the model with the number of objects and also the resulting load is greater as the resolution used increases."

"This book offers a comprehensive introduction to advanced methods for image and video analysis and processing. It covers deraining, dehazing, inpainting, fusion, watermarking and stitching. It describes techniques for face and lip recognition, facial expression recognition, lip reading in videos, moving object tracking, dynamic scene classification, among others.The book combines the latest machine learning methods with computer vision applications, covering topics such as event recognition based on deep learning，dynamic scene classification based on topic model, person re-identification based on metric learning and behavior analysis. It also offers a systematic introduction to image evaluation criteria showing how to use them in different experimental contexts.The book offers an example-based practical guide to researchers, professionals and graduate students dealing with advanced problems in image analysis and computer vision."

"Klasifikasi aksi multi-objek berdasarkan video RGB aerial merupakan tantangan kompleks yang dapat berguna untuk pengembangan sistem keamanan. Terdapat dua pendekatan jaringan saraf tiruan yang umum digunakan dalam sistem pengenal berbasis kerangka, Convolutional Neural Network (CNN) dan Graph Convolutional Network (GCN). Pendekatan CNN lebih efektif dalam mempelajari fitur spatio-temporal, lebih kuat terhadap noise dalam estimasi pose, dan dapat menangani skenario multi-objek dengan komputasi yang lebih ringan. Penelitian ini meliputi pengembangan pengenal aksi manusia dengan pendeteksi spatio-temporal berbasis kerangka menggunakan pendekatan 3D Convolutional Neural Network (3D-CNN). Pendeteksi spatio-temporal memungkinkan sistem untuk mengenali tiap-tiap aksi yang simultan dilakukan oleh multi-objek dalam satu rekaman video. Percobaan dilakukan menggunakan sejumlah pre-trained dataset dan menggunakan dataset video RGB aerial primer yang dilatih terhadap model pengenal aksi berbasis video frontal, dengan menerapkan metode transfer learning. Proses tranfer learning dilakukan dengan dataset khusus untuk menghasilkan model pelatihan yang memiliki akurasi tinggi. Pelatihan memberi keluaran berupa model jaringan saraf tiruan dengan nilai akurasinya. Pengujian dilakukan menggunakan data video untuk mengetahui ketepatan model. Dari model yang diperoleh, akan dilakukan analisis terhadap keberhasilan dan keakuratan metode dalam mengenali aksi manusia.

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Multi-object action recognition based on aerial RGB video is a complex challenge that can be useful for security system development. There are two commonly used artificial neural network approaches in skeleton-based recognition systems, Convolutional Neural Network (CNN) and Graph Convolutional Network (GCN). CNN approach is more effective in learning spatio-temporal features, more robust to noise in pose estimation, and can handle multi-object scenarios with lighter computation. This research involves developing a human action recognition with skeleton-based spatio-temporal detection using a 3D Convolutional Neural Network (3D-CNN) approach. Spatio-temporal detection allows the system to recognize each simultaneous action performed by multiple objects in a single video footage. Experiments were conducted using a number of pre-trained datasets and using a primary aerial RGB video dataset trained on a frontal video-based action recognition model, by applying the transfer learning method. The transfer learning process is performed with a specific dataset to produce a high-accuracy training model. The training outputs an artificial neural network model with its accuracy value. Testing is done using video data to determine the accuracy of the model. From the model obtained, the success and accuracy of the method in recognizing human actions will be analyzed."

"Closed Circuit TeleVision (CCTV) cameras have been increasingly deployed pervasively in public spaces including retail centres and shopping malls. Intelligent video analytics aims to automatically analyze content of massive amount of public space video data and has been one of the most active areas of computer vision research in the last two decades. Current focus of video analytics research has been largely on detecting alarm events and abnormal behaviours for public safety and security applications. However, increasingly CCTV installations have also been exploited for gathering and analyzing business intelligence information, in order to enhance marketing and operational efficiency. For example, in retail environments, surveillance cameras can be utilised to collect statistical information about shopping behaviour and preference for marketing (e.g., how many people entered a shop; how many females/males or which age groups of people showed interests to a particular product; how long did they stay in the shop; and what are the frequent paths), and to measure operational efficiency for improving customer experience. Video analytics has the enormous potential for non-security oriented commercial applications. This book presents the latest developments on video analytics for business intelligence applications. It provides both academic and commercial practitioners an understanding of the state-of-the-art and a resource for potential applications and successful practice."

"This book presents a broad review of state-of-the-art 3D video production technologies and applications. The text opens with a concise introduction to the field, before examining the design and calibration methods for multi-view camera systems, including practical implementation technologies. A range of algorithms are then described for producing 3D video from video data. A selection of 3D video applications are also demonstrated. Features : describes real-time synchronized multi-view video capture, and object tracking with a group of active cameras, discusses geometric and photometric camera calibration, and 3D video studio design with active cameras, examines 3D shape and motion reconstruction, texture mapping and image rendering, and lighting environment estimation; demonstrates attractive 3D visualization, visual contents analysis and editing, 3D body action analysis, and data compression, and highlights the remaining challenges and the exciting avenues for future research in 3D video technology."