RT Journal Article
T1 Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data
A1 Ullah, Waseem
A1 Ullah, Amin
A1 Hussain, Tanveer
A1 Muhammad, Khan
A1 Heidari, Ali Asghar
A1 Del Ser, Javier
A1 Baik, Sung Wook
A1 De Albuquerque, Victor Hugo C.
AB In the last few years, visual sensors are deployed almost everywhere, generating a massive amount of surveillance video data in smart cities that can be inspected intelligently to recognize anomalous events. In this work, we present an efficient and robust framework to recognize anomalies from surveillance Big Video Data (BVD) using Artificial Intelligence of Things (AIoT). Smart surveillance is an important application of AIoT and we propose a two-stream neural network in this direction. The first stream comprises instant anomaly detection that is functional over resource-constrained IoT devices, whereas second phase is a two-stream deep neural network allowing for detailed anomaly analysis, suited to be deployed as a cloud computing service. Firstly, a self-pruned fine-tuned lightweight convolutional neural network (CNN) classifies the ongoing events as normal or anomalous in an AIoT environment. Upon anomaly detection, the edge device alerts the concerned departments and transmits the anomalous frames to cloud analysis center for their detailed evaluation in the second phase. The cloud analysis center resorts to the proposed two-stream network, modeled from the integration of spatiotemporal and optical flow features through the sequential frames. Fused features flow through a bi-directional long short-term memory (BD-LSTM) layer, which classifies them into their respective anomaly classes, e.g., assault and abuse. We perform extensive experiments over benchmarks built on top of the UCF-Crime and RWF-2000 datasets to test the effectiveness of our framework. We report a 9.88% and 4.01% increase in accuracy when compared to state-of-the-art methods evaluated over the aforementioned datasets.
SN 0167-739X
YR 2022
FD 2022-04
LK https://hdl.handle.net/11556/5120
UL https://hdl.handle.net/11556/5120
LA eng
NO Ullah , W , Ullah , A , Hussain , T , Muhammad , K , Heidari , A A , Del Ser , J , Baik , S W & De Albuquerque , V H C 2022 , ' Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data ' , Future Generation Computer Systems , vol. 129 , pp. 286-297 . https://doi.org/10.1016/j.future.2021.10.033
NO Publisher Copyright: © 2021 Elsevier B.V.
NO This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067 ). All authors have read and agreed to the submitted version of the manuscript.
DS TECNALIA Publications
RD 28 sept 2024