Person re-identification, identifying and tracking pedestrians in cross-domain monitoring systems, is an important technology in the computer vision field and of real significance for the construction of smart cities. With the development of deep learning techniques, especially convolutional neural networks, this technology has received more extensive attention and improvement in recent years and a large number of noteworthy achievements have emerged. This paper provides a comprehensive overview of person re-identification approaches to assist researchers in quickly understand this field with preference as well as to provide a more structured framework. By reviewing more than 300 re-identification related papers, the focus of these studies is summarized as information extraction, metric learning, post-processing, efficiency improvement, labeling cost reduction, and data type expansion. This classification is then organized based on different technologies, and on this basis, the pros and cons of each technology are analyzed. Moreover, this overview summarizes the difficulties and challenges of re-identification and discusses the possible research directions for reference.
INDEX TERMSComputer vision, convolutional neural networks, distance learning, feature extraction, person re-identification.
This chapter constructs a system of financial ecological environment that is based on the multiple indicators: economic foundation, financial development, and institutional environment of 14 cities in Liaoning Province from 2008 to 2014. In addition, it supplements a measurement of influence of financial quality and uses the factor analysis method and panel threshold model to explore the dynamic evolution characteristics and optimization route of financial ecological environment. The empirical study shows that (1) the whole trend performs as an inversed U shape with the characteristics of rising at first and then declining and its influential mechanisms are disparate in different periods; (2) the financial ecological environment qualities of coastal cities rank in the upper and middle reaches of the overall rankings, while the qualities of inland cities comparatively fall behind; (3) the impact of urban financial ecological environment on economic growth in Liaoning Province presents as a non-linear single threshold, and the threshold values are 0.48, 0.52, 0.46, and 0.41, respectively.
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