## 2 Related Work  
Our work in this paper was based on general purpose smart city middleware. A few academic or research papers for general purpose smart city middleware and platform are available. So far, we have not been able to find any work that is completely similar to the work presented in our paper, even though there are good works for video surveillance itself and smart city itself. Thus, we believe that our paper presents a novel and unique system. In this related work section, we selected the works that were useful to understand the work in our paper or supplement the portion that our paper did not deal with or that our paper did not give a detailed explanation for. A video surveillance system for smart cities must be able to process big data that is continuously generated by video cameras. Traditional distributed system technology could not handle big data properly [7; 8; 9]. Cloud computing successfully helps us to manage big data, and novel technology based on the Cloud seems to be a new trend in video surveillance systems. [PERSON] et al. [10] present a study on the implementation of image processing algorithms in various types of computer systems. In comparison, our paper does not deal with various computer vision algorithms, but only explains our used algorithm. So, we asked readers who are interested in the image processing algorithms to refer to [10]. [PERSON] et al. [10] present their study for data mining and do not deal with a smart city and any middleware for smart cities, but our paper is for a smart city and based on a middleware for smart cities. [PERSON] et al. [11] suggest split and merge architecture that reduces video encoding time. Our paper presents a middleware platform based approach for smart cities, but [PERSON] et al. [11] do not. [PERSON] et al. [12] propose an algorithm for 3D conversion from traffic video content to Google Map. Time-stamped glyph-based visualization is used in outdoor surveillance videos for the algorithm, which can be used for event-aware detection. In comparison, our paper dealt with object detection with MapReduce, but [PERSON] et al. do not, and our paper dealt with real-time scalable video streaming using a smart city middleware, but [PERSON] et al. do not. [PERSON] et al. [13] propose an algorithm for a media-based surveillance system to improve media security and privacy of wireless sensor networks in the Internet of Things (IoT) network for a smart city framework. Compared to their paper, our paper proposed the USVS system as an all-in-one system for object detection with MapReduce rather than an algorithm for a media-based surveillance system and our paper did not deal with media security or the privacy of wireless sensor networks. [PERSON] et al. [14] discuss the standard for the future deep feature coding to manage the AI-oriented large-scale video, existing techniques, standards, and the possible large-scale video analysis solutions for deep neural network models. In comparison, their paper focuses on deep learning with neural network models, but our paper proposed an all-in-one system of USVS, which includes video surveillance, scalable video streaming, Cloud computing, a middleware platform (SOUL) for a smart city, and a smart city paradigm (UTOPIA), and dealt with image processing for object detection with MapReduce using USVS. [PERSON] et al. [15] propose a block-level background modeling algorithm to support a long-term reference structure for efficient surveillance video coding and a rate-distortion optimization for the surveillance source algorithm to process big video data in a smart city. In comparison, their paper focuses on a block-level background modeling algorithm, but our paper proposed an all-in-one system of USVS, which includes video surveillance, scalable video streaming, Cloud computing, a middleware platform (SOUL) for a smart city, and a smart city paradigm (UTOPIA), and dealt with image processing for object detection with MapReduce using USVS. [PERSON] et al.