Paper Info
Title
An Efficient Anomaly Recognition Framework Using An Attention Residual Lstm In Surveillance Videos
Abstract
Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model's effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.
Year
DOI
Venue
2021
10.3390/s21082811
SENSORS
Keywords
DocType
Volume
anomaly detection, video surveillance system, abnormal activity recognition, attention mechanism, LSTM, residual LSTM, deep learning, smart surveillance, crime recognition
Journal
21
Issue
ISSN
Citations 
8
1424-8220
1
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Waseem Ullah110.34
Amin Ullah210911.60
Tanveer Hussain3667.99
Zulfiqar Ahmad Khan411.35
Sung Wook Baik512.03