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

Abnormal Event Detection In Video Using Deep Learning

Authors: Deekshitha KN S., Harshitha k., Nakshatra gowda, Mohammed ibrahim, Narayana M H, Aruna M G, Dr. Malatesh S H

Unique Paper ID: 156219
Volume: 9
Issue: 3
PageNo: 276-279

Keywords: Deep learning Neural Network Convolutional Neural Network(CNN) Long Short Term Memory(LSTM) ConvLSTM Pre-processing and Thresholding .

Abstract:
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

Download article

email to a friend

Cite This Article

ISSN: 2349-6002
Volume: 9
Issue: 3
PageNo: 276-279

Abnormal Event Detection In Video Using Deep Learning

Available:https://ijirt.org/Article?manuscript=156219

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now