TY - JOUR
T1 - Artificial Intelligence of Things-assisted two-stream neural network for anomaly detection in surveillance Big Video Data
AU - Ullah, Waseem
AU - Ullah, Amin
AU - Hussain, Tanveer
AU - Muhammad, Khan
AU - Heidari, Ali Asghar
AU - Del Ser, Javier
AU - Baik, Sung Wook
AU - De Albuquerque, Victor Hugo C.
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/4
Y1 - 2022/4
N2 - 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.
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.
KW - Anomaly detection
KW - Anomaly recognition
KW - Surveillance videos
KW - Two-stream network
UR - http://www.scopus.com/inward/record.url?scp=85121546312&partnerID=8YFLogxK
U2 - 10.1016/j.future.2021.10.033
DO - 10.1016/j.future.2021.10.033
M3 - Article
AN - SCOPUS:85121546312
SN - 0167-739X
VL - 129
SP - 286
EP - 297
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -