Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks published, there are not many papers that can provide a comprehensive picture of the current state of video surveillance system research. This paper provides a comprehensive and systematic review on the literature from various video surveillance system studies published from 2010 through 2019. Within a selected study extraction process, 220 journal-based publications were identified and analyzed to illustrate the research trends, datasets, methods, and frameworks used in the field of video surveillance, to provide an in-depth explanation about research trends that many topics raised by researchers as a focus in their researches, to provide references on public datasets that are often used by researchers as a comparison and a means of developing a test method, and to give accounts on the improvement and integration of network infrastructure design to meet the demand for multimedia data. In the end of this paper, several opportunities and challenges related to researches in the video surveillance system are mentioned.INDEX TERMS Artificial intelligence, cloud video surveillance, intelligent video surveillance, video surveillance framework.This study is conducted as follows: the methodology of the study is presented in Section 2. The outcomes and answers to the research questions are then discussed in Section 3. Finally, the study is summarized in the last section.

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From literature reviews, the marine environment influences the quality of underwater images and makes the identification of fish species more complex and challenging. The images of the marine environment have low image quality that causes the generated features to be reduced; therefore, this decreases the performance of the classification method. To the best knowledge of the authors, we found out that many researchers have focussed only on determining identification methods without considering the quality of the original data. Therefore, the impact of image enhancement toward the accuracy is yet to be known because this has not been studied comprehensively. To deal with this research gap we propose a new workflow of fish species identification. The workflow for our proposed approach is by using the gray-level co-occurrence matrix (GLCM) feature extraction fed into the back-propagation neural network (BPNN) with contrast-adaptive color correction technique (NCACC) as image enhancements. The experiments demonstrated an improvement in accuracy and kappa measurements for fish species identification from 4.68% to 93.73% and improve from 0.05 to 0.92 respectively. Therefore, our proposed method has the potential to support automatic fish identification systems based on computer vision technology.

Suatu data atau informasi disajikan tidak hanya berupa data teks tetapi juga dapat berupa audio, video, dan gambar. Pada zaman sekarang informasi sangatlah penting dan diperlukan, begitu juga informasi yang terdapat pada citra. Citra (image) atau istilah lain untuk gambar merupakan salah satu komponen multimedia yang berperan penting sebagai bentuk informasi visual. Dibandingkan dengan data teks, citra memiliki banyak informasi. Namun terkadang citra juga dapat mengalami penurunan yaitu degradasi atau penurunan kualitas yang disebabkan oleh derau / noise, warna terlalu kontras, kabur, dan lain-lain. Ada beberapa jenis noise dalam pengolahan citra salah satunya yaitu Salt & Pepper noise. Noise Salt & Pepper berbentuk seperti bintik hitam dan putih pada citra. Untuk mengurangi noise ini dibutuhkan suatu metode, salah satunya yaitu median filter. Metode yang digunakan pada penelitian ini adalah median filter dan adaptif median filter. Perbedaan mendasar antara kedua metode ini yaitu pada besarnya windows pada adaptif median filter adalah variabel. Dari hasil penelitian, citra yang menggunakan metode adaptif median filter lebih baik daripada median filter. Dari perhitungan Peak Signal to Noise Ratio (PSNR) citra yang menggunakan adaptif median filter mendapatkan 29,2495 dB sedangkan median filter mendapatkan 23,8181 dB.Kata Kunci: Median filter, Adaptif Median filter, Noise salt & pepper, PSNR

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This research describes a 3D reconstruction method of coral reefs using low-cost underwater cameras. We employed a multi-view camera system consisting of 3 identical waterproof cameras arrayed on a stereo-base, and collected footage of the seafloor in linear transects. To develop a 3D-representation of the seafloor image-pairs were fIrst extracted from the video footage manually. Then corresponding points are automatically extracted from the stereo-pairs by the well known SIFT algorithm, which is invariant to scale, translation, and rotation. Ba sed on the resultant x,y,z point cloud the 3D appearance of the coral reef is approximated by a Triangulation technique utilizing Delaunay Triangulation. The experimental result demonstrate robust 3D reconstruction with manual adjustment of camera A, B or C selection.

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Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.