TY - GEN
T1 - Intelligent moving objects detection via adaptive frame differencing method
AU - Tsai, Chun Ming
AU - Yeh, Zong Mu
PY - 2013
Y1 - 2013
N2 - The detection of moving objects is a critical first step in video surveillance, but conventional moving objects detection methods are not efficient or effective for certain types of moving objects: slow and fast. This paper presents an intelligent method to detect slow- and fast-moving objects simultaneously. It includes adaptive frame differencing, automatic thresholding, and moving objects localization. The adaptive frame differencing uses different inter-frames for frame differencing, the number depending on variations in the differencing image. The thresholding method uses a modified triangular algorithm to determine the threshold value and reduces most small noises. The moving objects localization uses six cascaded rules and bounding-boxes-based morphological operations to merge broken objects and remove noise objects. The fps value (maximum 72) depends on the speed of the objects. The number of inter-frames is inversely proportional to the speed. The results demonstrate that our method is more efficient than traditional frame differencing and background subtraction methods.
AB - The detection of moving objects is a critical first step in video surveillance, but conventional moving objects detection methods are not efficient or effective for certain types of moving objects: slow and fast. This paper presents an intelligent method to detect slow- and fast-moving objects simultaneously. It includes adaptive frame differencing, automatic thresholding, and moving objects localization. The adaptive frame differencing uses different inter-frames for frame differencing, the number depending on variations in the differencing image. The thresholding method uses a modified triangular algorithm to determine the threshold value and reduces most small noises. The moving objects localization uses six cascaded rules and bounding-boxes-based morphological operations to merge broken objects and remove noise objects. The fps value (maximum 72) depends on the speed of the objects. The number of inter-frames is inversely proportional to the speed. The results demonstrate that our method is more efficient than traditional frame differencing and background subtraction methods.
KW - Adaptive frame differencing
KW - Bounding-boxes-based morphological operations
KW - Moving objects Detection
KW - Video surveillance
KW - background subtraction
UR - http://www.scopus.com/inward/record.url?scp=84874642148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874642148&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-36546-1_1
DO - 10.1007/978-3-642-36546-1_1
M3 - Conference contribution
AN - SCOPUS:84874642148
SN - 9783642365454
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 11
BT - Intelligent Information and Database Systems - 5th Asian Conference, ACIIDS 2013, Proceedings
T2 - 5th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2013
Y2 - 18 March 2013 through 20 March 2013
ER -