Titlebook: Deep Learning and Big Data for Intelligent Transportation; Enabling Technologie Khaled R. Ahmed,Aboul Ella Hassanien Book 2021 The Editor(s

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Michael Heidt,Andreas Bischof,Paul Rosenthalturbances. In most cases, the existing moving object detection approaches concentrate only on the foreground information and frequently ignore the background information. As a result, trackers will be deviated away from the target and detect the non-foreground objects. Recently, several contribution

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Athanasios Karapantelakis,Yonghui Guootal number of people in a crowd image is needed for the hour. In this chapter, we have reviewed crowd count methods using state of art deep learning models for automated crowd count and their performance analysis on major crowd counting datasets.

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Mary C. Dyson,Elizabeth M. Jenningscomputing model that has intensified the growth of information technology. It enchanted the sprout of IoT as it needs more storage for the data that are acquired from the objects. Another booming technology is artificial intelligence where the intelligence of machine is used for enabling smart tasks

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Big Data Technologies with Computational Model Computing Using Hadoop with Scheduling Challengesof-craftsmanship survey that gives an all-encompassing perspective on the BD difficulties, and BDA techniques speculated/proposed/ utilized associations to help other people comprehend this scene to settle on strong venture choices. The examination introduced in this part has recognized significant

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Analysis of Target Detection and Tracking for Intelligent Vision Systemturbances. In most cases, the existing moving object detection approaches concentrate only on the foreground information and frequently ignore the background information. As a result, trackers will be deviated away from the target and detect the non-foreground objects. Recently, several contribution