Performance Analysis of Hybrid RR Algorithm for Anomaly Detection in Streaming Data

Abstract

Automated live video stream analytics has been extensively researched in recent times. Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame. We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). In the first stage, an ensemble of LSTM-RNNs are deployed to generate the anomaly score. The second stage uses the least square method for optimal anomaly score generation. The third stage adopts award-based reinforcement learning to update the model. The proposed Hybrid Ensemble RR Model was tested on standard pedestrian datasets UCSDPed1, USDPed2. The data set has 70 videos in UCSD Ped1 and 28 videos in UCSD Ped2 with a total of 18560 frames. Since a real-time stream has strict memory constraints and storage issues, a simple computing machine does not suffice in performing analytics with stream data. Hence the proposed research is designed to work on a GPU (Graphics Processing Unit), TPU (Tensor Processing Unit) supported framework. As shown in the experimental results section, recorded observations on frame-level EER (Equal Error Rate) and AUC (Area Under Curve) showed a 9% reduction in EER in UCSD Ped1, a 13% reduction in ERR in UCSD Ped2 and a 4% improvement in accuracy in both datasets.