A comprehensive review on deep learning-based methods for video anomaly detection
Video surveillance systems are popular and used in public places such as market places,
shopping malls, hospitals, banks, streets, education institutions, city administrative offices …
shopping malls, hospitals, banks, streets, education institutions, city administrative offices …
Video processing using deep learning techniques: A systematic literature review
Studies show lots of advanced research on various data types such as image, speech, and
text using deep learning techniques, but nowadays, research on video processing is also an …
text using deep learning techniques, but nowadays, research on video processing is also an …
Anomaly detection in video via self-supervised and multi-task learning
MI Georgescu, A Barbalau… - Proceedings of the …, 2021 - openaccess.thecvf.com
Anomaly detection in video is a challenging computer vision problem. Due to the lack of
anomalous events at training time, anomaly detection requires the design of learning …
anomalous events at training time, anomaly detection requires the design of learning …
SGCN: Sparse graph convolution network for pedestrian trajectory prediction
Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very
challenging due to complex interactions between pedestrians. However, previous works …
challenging due to complex interactions between pedestrians. However, previous works …
Human trajectory forecasting in crowds: A deep learning perspective
Since the past few decades, human trajectory forecasting has been a field of active research
owing to its numerous real-world applications: evacuation situation analysis, deployment of …
owing to its numerous real-world applications: evacuation situation analysis, deployment of …
Graph neural networks for anomaly detection in industrial Internet of Things
The Industrial Internet of Things (IIoT) plays an important role in digital transformation of
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …
CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks
In current technological era, surveillance systems generate an enormous volume of video
data on a daily basis, making its analysis a difficult task for computer vision experts …
data on a daily basis, making its analysis a difficult task for computer vision experts …
Lanercnn: Distributed representations for graph-centric motion forecasting
Forecasting the future behaviors of dynamic actors is an important task in many robotics
applications such as self-driving. It is extremely challenging as actors have latent intentions …
applications such as self-driving. It is extremely challenging as actors have latent intentions …
Robust unsupervised video anomaly detection by multipath frame prediction
Video anomaly detection is commonly used in many applications, such as security
surveillance, and is very challenging. A majority of recent video anomaly detection …
surveillance, and is very challenging. A majority of recent video anomaly detection …
A background-agnostic framework with adversarial training for abnormal event detection in video
Abnormal event detection in video is a complex computer vision problem that has attracted
significant attention in recent years. The complexity of the task arises from the commonly …
significant attention in recent years. The complexity of the task arises from the commonly …