TY - GEN
T1 - Image fusion and influence function for performance improvement of ATM vandalism action recognition
AU - Yun, Jeongseop
AU - Lee, Junyeop
AU - Mun, Seongkyu
AU - Cho, Chul Jin
AU - Han, David K.
AU - Ko, Hanseok
N1 - Funding Information:
This work was supported in part by the National Research Foundation (NRF) grant funded by the MSIP (No. 2017R1A2B4012720) and by the US Army Research Laboratory.
PY - 2019/2/11
Y1 - 2019/2/11
N2 - Rising rate of vandalism against Automatic Teller Machines (ATMs) is a serious issue within banking industries, prompting needs of a technology to autonomously recognize such events. A vision based fusion method proposed here for classifying these incidents is rooted on visually recognizing heavy or sharp objects potentially used for detecting vandalism actions inferred from optical flow. The recognition performance has been improved chiefly by a novel employment of influence functions in selecting data points of each class useful in learning. We show that the tool recognition performance can be improved when the training data is selected from the ImageNet data set as guided by the influence function.
AB - Rising rate of vandalism against Automatic Teller Machines (ATMs) is a serious issue within banking industries, prompting needs of a technology to autonomously recognize such events. A vision based fusion method proposed here for classifying these incidents is rooted on visually recognizing heavy or sharp objects potentially used for detecting vandalism actions inferred from optical flow. The recognition performance has been improved chiefly by a novel employment of influence functions in selecting data points of each class useful in learning. We show that the tool recognition performance can be improved when the training data is selected from the ImageNet data set as guided by the influence function.
UR - http://www.scopus.com/inward/record.url?scp=85063269401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063269401&partnerID=8YFLogxK
U2 - 10.1109/AVSS.2018.8639091
DO - 10.1109/AVSS.2018.8639091
M3 - Conference contribution
AN - SCOPUS:85063269401
T3 - Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
BT - Proceedings of AVSS 2018 - 2018 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2018
Y2 - 27 November 2018 through 30 November 2018
ER -