586 research outputs found
A Survey of Deep Learning Solutions for Anomaly Detection in Surveillance Videos
Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well
Anomaly Detection in Aerial Videos with Transformers
Unmanned aerial vehicles (UAVs) are widely applied for purposes of
inspection, search, and rescue operations by the virtue of low-cost,
large-coverage, real-time, and high-resolution data acquisition capacities.
Massive volumes of aerial videos are produced in these processes, in which
normal events often account for an overwhelming proportion. It is extremely
difficult to localize and extract abnormal events containing potentially
valuable information from long video streams manually. Therefore, we are
dedicated to developing anomaly detection methods to solve this issue. In this
paper, we create a new dataset, named DroneAnomaly, for anomaly detection in
aerial videos. This dataset provides 37 training video sequences and 22 testing
video sequences from 7 different realistic scenes with various anomalous
events. There are 87,488 color video frames (51,635 for training and 35,853 for
testing) with the size of at 30 frames per second. Based on
this dataset, we evaluate existing methods and offer a benchmark for this task.
Furthermore, we present a new baseline model, ANomaly Detection with
Transformers (ANDT), which treats consecutive video frames as a sequence of
tubelets, utilizes a Transformer encoder to learn feature representations from
the sequence, and leverages a decoder to predict the next frame. Our network
models normality in the training phase and identifies an event with
unpredictable temporal dynamics as an anomaly in the test phase. Moreover, To
comprehensively evaluate the performance of our proposed method, we use not
only our Drone-Anomaly dataset but also another dataset. We will make our
dataset and code publicly available. A demo video is available at
https://youtu.be/ancczYryOBY. We make our dataset and code publicly available
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos
Deep learning models have been widely used for anomaly detection in
surveillance videos. Typical models are equipped with the capability to
reconstruct normal videos and evaluate the reconstruction errors on anomalous
videos to indicate the extent of abnormalities. However, existing approaches
suffer from two disadvantages. Firstly, they can only encode the movements of
each identity independently, without considering the interactions among
identities which may also indicate anomalies. Secondly, they leverage
inflexible models whose structures are fixed under different scenes, this
configuration disables the understanding of scenes. In this paper, we propose a
Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to
address these problems, the HSTGCNN is composed of multiple branches that
correspond to different levels of graph representations. High-level graph
representations encode the trajectories of people and the interactions among
multiple identities while low-level graph representations encode the local body
postures of each person. Furthermore, we propose to weightedly combine multiple
branches that are better at different scenes. An improvement over single-level
graph representations is achieved in this way. An understanding of scenes is
achieved and serves anomaly detection. High-level graph representations are
assigned higher weights to encode moving speed and directions of people in
low-resolution videos while low-level graph representations are assigned higher
weights to encode human skeletons in high-resolution videos. Experimental
results show that the proposed HSTGCNN significantly outperforms current
state-of-the-art models on four benchmark datasets (UCSD Pedestrian,
ShanghaiTech, CUHK Avenue and IITB-Corridor) by using much less learnable
parameters.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video
Technology (T-CSVT
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