gist descriptor
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2018 ◽  
Vol 21 (1) ◽  
pp. 153-163 ◽  
Author(s):  
Joshua Myrans ◽  
Richard Everson ◽  
Zoran Kapelan

Abstract Sewers must be regularly inspected to prioritise effective maintenance, which can be an expensive and time-consuming process. This paper presents a methodology to automatically identify the type of a detected fault using raw closed circuit television (CCTV) footage. The procedure calculates the GIST descriptor of a video frame containing a fault before applying a collection of random forest classifiers to identify the fault's type. Order oblivious filtering is used to further improve the methodology's performance on continuous footage. The technology, including various classifier architectures, has been validated and demonstrated on CCTV footage collected by Wessex Water. The methodology achieved a peak accuracy of 73% when applied to well-represented fault types, showing promise for future application in the water industry.



2016 ◽  
Vol 79 ◽  
pp. 533-542 ◽  
Author(s):  
Vinay A. ◽  
Gagana B. ◽  
Vinay.S. Shekhar ◽  
Anil B. ◽  
K.N. Balasubramanya Murthy ◽  
...  


2013 ◽  
Vol 29 (1) ◽  
pp. 146-160 ◽  
Author(s):  
Ana C. Murillo ◽  
Gautam Singh ◽  
Jana Kosecká ◽  
J. J. Guerrero


2008 ◽  
Author(s):  
Pei Tao ◽  
Chu He ◽  
Chao Qian ◽  
Hong Sun


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