641 research outputs found
Deep Neural Network based Anomaly Detection for Real Time Video Surveillance
One of the main concerns across all kinds of domains has always been security. With the crime rates increasing every year the need to control has become crucial. Among the various methods present to monitor crime or any anomalous behavior is through video surveillance. Nowadays security cameras capture incidents in almost all public and private place if desired. Even though we have abundance of data in the form of videos they need to be analyzed manually. This results in long hours of manual labour and even small human discrepancies may have huge consequences negatively. For this purpose, a Convolution Neural Network (CNN) based model is built to detect any form of abnormal activities or anomalies in the video footages. This model converts the input video into frames and detects the anomalous frames. To increase the efficiency of the model, the data is de-noised with Gaussian blur feature. The avenue dataset is used in this work to detect and predict various kinds of anomalies. The performance of the model is measured using classification accuracy and the results are reported
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
- …