TY - GEN
T1 - A kidnapping detection scheme using frame-based classification for intelligent video surveillance
AU - Gwon, Ryu Hyeok
AU - Kim, Kyoung Yeon
AU - Park, Jin Tak
AU - Kim, Hakill
AU - Kim, Yoo Sung
PY - 2013
Y1 - 2013
N2 - The purpose of this study is to develop a kidnapping event detection scheme for intelligent video surveillance by frame-based classification which is able to assort each frame into a kidnapping or normally accompanying situation. In this study, for generating training data from videos, a semi-automatic video annotation tool named INHA-VAT is used. Also, we developed a frame-based event classifier using Bayesian network model to distinguish the frame of kidnapping situations from one of accompanying ones. When a video has more frames of kidnapping situation than the threshold ratio after two people meet in the video, the proposed scheme detects and notifies the occurrence of kidnapping event. To check the feasibility of the proposed scheme, we also performed the accuracy evaluation against test videos. According to the experiment results, the proposed scheme could detect kidnapping situations appropriately according to the threshold ratio.
AB - The purpose of this study is to develop a kidnapping event detection scheme for intelligent video surveillance by frame-based classification which is able to assort each frame into a kidnapping or normally accompanying situation. In this study, for generating training data from videos, a semi-automatic video annotation tool named INHA-VAT is used. Also, we developed a frame-based event classifier using Bayesian network model to distinguish the frame of kidnapping situations from one of accompanying ones. When a video has more frames of kidnapping situation than the threshold ratio after two people meet in the video, the proposed scheme detects and notifies the occurrence of kidnapping event. To check the feasibility of the proposed scheme, we also performed the accuracy evaluation against test videos. According to the experiment results, the proposed scheme could detect kidnapping situations appropriately according to the threshold ratio.
KW - Bayesian network
KW - Kidnapping detection
KW - discriminative features
KW - frame-based event classification
KW - intelligent video surveillance
UR - http://www.scopus.com/inward/record.url?scp=84887482550&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41218-9_37
DO - 10.1007/978-3-642-41218-9_37
M3 - Conference contribution
AN - SCOPUS:84887482550
SN - 9783642412172
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 345
EP - 354
BT - Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - 14th International Conference, RSFDGrC 2013, Proceedings
T2 - 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2013
Y2 - 11 October 2013 through 14 October 2013
ER -