Abnormal Event Detection In Video Using Deep Learning
Deekshitha KN S., Harshitha k., Nakshatra gowda, Mohammed ibrahim, Narayana M H, Aruna M G, Dr. Malatesh S H
Deep learning, Neural Network, Convolutional Neural Network(CNN), Long Short Term Memory(LSTM) , ConvLSTM , Pre-processing and Thresholding .
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 spatio-temporal 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
Article Details
Unique Paper ID: 156219

Publication Volume & Issue: Volume 9, Issue 3

Page(s): 276 - 279
Article Preview & Download

Share This Article

Join our RMS

Conference Alert


AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management


Last Date: 7th November 2023

Call For Paper

Volume 10 Issue 10

Last Date for paper submitting for March Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews