Suspicious Activity Detection in Surveillance Video Using Fully Convolutional Networks Segmentation
Keywords:
Segmentation, FCN, Object Modeling, Suspicious Activity Detection, Surveillance VideoAbstract
In Recent Years, suspicious activity detection is used to detect traffic in different surveillance videos with high accuracy and high speed in daytime. This surveillance video detection method includes Adaptive Background, Object Modeling, Object Tracking, Activity Recognition, and Segmentation. The semantic segmentation using suspicious activity detection techniques plays a major role in the segmentation of the surveillance video. U-Net is one of the popular Fully Convolutional Networks (FCN) which is applicable for image segmentation. This method could found the different anomalies activity from the videos.
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