Verbal and Object Counting

2020 ◽  
pp. 36-66
Author(s):  
Douglas H. Clements ◽  
Julie Sarama
Keyword(s):  
IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 2529-2541 ◽  
Author(s):  
Yung-Wei Chen ◽  
Kai Chen ◽  
Shih-Yi Yuan ◽  
Sy-Yen Kuo

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.


Author(s):  
Hao Shang ◽  
Rui Li ◽  
Xu He ◽  
Jilong Wang ◽  
Xinhui Peng
Keyword(s):  

2020 ◽  
Vol 210 ◽  
pp. 106485
Author(s):  
He Li ◽  
Weihang Kong ◽  
Shihui Zhang
Keyword(s):  

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