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Night Vision Object Tracking System Using correlation aware lstm-based Modified yolo algorithm

Intelligent Automation & Soft Computing 2023年第4期36卷 353-368页

作者： R.Anandha Murugan B.Sathyabama Department of Computer Science and Engineering K.L.N.College of Engineering630612TamilnaduIndia Department of Electronics and Communication Engineering Thiagarajar College of EngineeringMadurai625015TamilnaduIndia

Improved picture quality is critical to the effectiveness of object recog-nition and *** consistency of those photos is impacted by night-video systems because the contrast between high-proﬁle items and different atmospheric conditions,such as mist,fog,dust *** pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient *** recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance ***,the iden-tiﬁcation of objects in the night world also poses further problems because of the distorted backdrop and dim *** correlation aware lstm based yolo(You Look Only Once)classiﬁer method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this *** order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerﬁlter for removing the noise in the *** process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set ***,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(lstm)with yolo method for object tracking system.A range of experimentalﬁndings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mec

关键词： Object monitoring night vision image SSAN dataset adaptive internal linear embedding uplift linear discriminant analysis recurrent-phase level set segmentation correlation aware lstm based yolo classiﬁer algorithm

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