## 2 Related Work
[16] present optimization algorithms and scheduling strategies based on an Unmanned Aerial Vehicle (UAV) cluster. In comparison, 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. [17] propose an intelligent video surveillance system that can detect and identify abnormal and alarming situations by analyzing object movement in smart cities. It does ontology based semantic translation. Compared to our paper, it does not mention any smart city middleware, nor it does mention that it uses Cloud computing. [PERSON] et al. [18] present a study on virtual machine resource allocation to support video surveillance platforms that uses the Amazon Web Service to process linear programming formulas with migration control and simulation. They focus on Cloud computing. Compared to our paper, their work does not deal with MapReduce, scalable video streaming, and any smart city middleware. They do not mention smart city. On the other hand, 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. [19] propose a video recorder system based on Cloud. A digital video recorder and network video recorder with their methodology are suggested. Their distributed replication mechanism uses the Hadoop distributed file system and handles the deployment design of public and private/hybrid Clouds. In comparison, they focus on the video recorder system and do not deal with image processing for object detection, but we did, and they do not mention MapReduce but we did use MapReduce. We 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), but they do not. [PERSON] et al. [20] propose a system that has a peer-to-peer architecture with the Hadoop distributed file system to solve the network bottlenecks that centralized video data processing systems typically have. They divide the input video source by chunk size, however we divided the input video source by the video static Group-Of-Picture (GOP) length. The advantage of our approach is explained in detail in Section 5. They focus on the P2P and Cloud in their architecture and its operation, but they do not mention MapReduce, any smart city middleware, or a smart city. We 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). [PERSON] et al. [21] propose a Cloud-based traditional video surveillance architecture that consists of a Cloud processing server, a Cloud storage server and a web server. They stored a video in Amazon S3 storage. They do not mention any smart city middleware or smart city. On the other hand, 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. [22] present an intelligent processing and utilization solution to big surveillance video data based on the event detection and alarming messages from front-end smart cameras. In comparison, they do not deal with MapReduce and do not mention any smart city middleware.