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1篇 您的检索式：作者名="M.Indirani"

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		题名	作者	年代	出处	被引量
	1	Ensemble Based Learning with Accurate Motion Contrast Detection显示文摘Recent developments in computer vision applications have enabled detection of signiﬁcant visual objects in video streams.Studies quoted in literature have detected objects from video streams using Spatiotemporal Particle Swarm Optimization(SPSOM)and Incremental Deep Convolution Neural Networks(IDCNN)for detecting multiple objects.However,the study considered opticalﬂows resulting in assessing motion contrasts.Existing methods have issue with accuracy and error rates in motion contrast detection.Hence,the overall object detection performance is reduced signiﬁcantly.Thus,consideration of object motions in videos efﬁciently is a critical issue to be solved.To overcome the above mentioned problems,this research work proposes a method involving ensemble approaches to and detect objects efﬁciently from video streams.This work uses a system modeled on swarm optimization and ensemble learning called Spatiotemporal Glowworm Swarm Optimization Model(SGSOM)for detecting multiple signiﬁcant objects.A steady quality in motion contrasts is maintained in this work by using Chebyshev distance matrix.The proposed system achieves global optimization in its multiple object detection by exploiting spatial/temporal cues and local constraints.Its experimental results show that the proposed system scores 4.8%in Mean Absolute Error(MAE)while achieving 86%in accuracy,81.5%in precision,85%in recall and 81.6%in F-measure and thus proving its utility in detecting multiple objects.	M.Indirani S.Shankar	2023	Intelligent Automation & Soft Computing2023，，2：	0

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