RETRACTED: Efficient anomaly detection in surveillance videos based on multi layer perception recurrent neural network

Abstract

Surveillance frameworks actualized in true environment are strong in nature. As the environment is uncertain and dynamic, the surveillance turns out to be increasingly perplexing when contrasted with a static and controlled environment. Effective anomaly identification in the video surveillance is a difficult issue because of spilling, video noise, anomalies, and goals. This examination work proposes a background deduction approach dependent on Maximally Stable Extremal Region (MSER) highlight extraction technique with the ongoing profound learning structure of Multi-layer perception recurrent neural network (MLP-RNN) that is fit for distinguishing multiple objects of various sizes by pixel-wise foreground investigating framework. The proposed algorithm takes as information a reference (without anomaly) and an objective edge, both transiently adjusted, and outputs a segmentation guide of same spatial goals where the featured pixels meaning the recognized anomalies, which ought to be all the components not present in the reference outline. Besides, examine the advantages of various remaking strategies to the reestablish unique picture goals and exhibit the improvement of leftover designs over the littler and more straightforward models proposed by past comparable works. The simulation results are shows serious execution in the tried dataset, just as constant handling ability as compared with existing methods.