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      City brain: practice of large-scale artificial intelligence in the real world

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          Abstract

          A city is an aggregate of a huge amount of heterogeneous data. However, extracting meaningful values from that data remains a challenge. City Brain is an end-to-end system whose goal is to glean irreplaceable values from big city data, specifically from videos, with the assistance of rapidly evolving artificial intelligence technologies and fast-growing computing capacity. From cognition to optimisation, to decision-making, from search to prediction and ultimately, to intervention, City Brain improves the way to manage the city, as well as the way to live in it. In this study, the authors introduce current practices of the City Brain platform in a few cities in China, including what they can do to achieve the goal and make it a reality. Then they focus on the system overview and key technical details of each component of the City Brain system, from cognition to intervention. Lastly, they present a few deployment cases of City Brain in various cities in China.

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          Most cited references 9

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          High-Speed Tracking with Kernelized Correlation Filters

          The core component of most modern trackers is a discriminative classifier, tasked with distinguishing between the target and the surrounding environment. To cope with natural image changes, this classifier is typically trained with translated and scaled sample patches. Such sets of samples are riddled with redundancies-any overlapping pixels are constrained to be the same. Based on this simple observation, we propose an analytic model for datasets of thousands of translated patches. By showing that the resulting data matrix is circulant, we can diagonalize it with the discrete Fourier transform, reducing both storage and computation by several orders of magnitude. Interestingly, for linear regression our formulation is equivalent to a correlation filter, used by some of the fastest competitive trackers. For kernel regression, however, we derive a new kernelized correlation filter (KCF), that unlike other kernel algorithms has the exact same complexity as its linear counterpart. Building on it, we also propose a fast multi-channel extension of linear correlation filters, via a linear kernel, which we call dual correlation filter (DCF). Both KCF and DCF outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite running at hundreds of frames-per-second, and being implemented in a few lines of code (Algorithm 1). To encourage further developments, our tracking framework was made open-source.
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            Real-time road traffic prediction with spatio-temporal correlations

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              Multi-Task Vehicle Detection With Region-of-Interest Voting

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                Author and article information

                Contributors
                Journal
                IET-SMC
                IET Smart Cities
                IET Smart Cities
                The Institution of Engineering and Technology
                2631-7680
                28 May 2019
                24 June 2019
                June 2019
                : 1
                : 1
                : 28-37
                Affiliations
                [1 ] DAMO Academy, Alibaba Group , 969 West Wenyi Road, Hangzhou, Zhejiang Province, People's Republic of China
                [2 ] Shanghai Jiao Tong University , 800 Dongchuan Road, Shanghai, People's Republic of China
                Article
                IET-SMC.2019.0034 SMC.2019.0034
                10.1049/iet-smc.2019.0034

                This is an open access article published by the IET under the Creative Commons Attribution -NonCommercial License ( http://creativecommons.org/licenses/by-nc/3.0/)

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                Pages: 0
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