Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{156219,
        author = {Deekshitha KN S. and Harshitha k. and Nakshatra gowda and Mohammed ibrahim and Narayana M H and Aruna M G and Dr. Malatesh S H},
        title = {Abnormal Event Detection In Video Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {3},
        pages = {276-279},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156219},
        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},
        keywords = {Deep learning, Neural Network, Convolutional Neural Network(CNN), Long Short Term Memory(LSTM) , ConvLSTM , Pre-processing and Thresholding .},
        month = {},
        }