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Video Classification using SlowFast Network via Fuzzy rule
ISSN
10987584
Date Issued
2021-07-11
Author(s)
Rituraj,
Tiwari, Aruna
Chaudhury, Santanu
Singh, Sanjay
Saurav, Sumeet
DOI
10.1109/FUZZ45933.2021.9494542
Abstract
Anomalous events occur rarely and are challenging to model. Therefore, automatic recognition of abnormal activities in surveillance videos is a non-trivial task. Though with the availability of video datasets of abnormal activities, there has been some progress, recognition of abnormal activities in real-time with high confidence remains unsolved. Existing video-based anomaly detection techniques using traditional machine learning and deep-learning are compute-intensive and give low recognition accuracy. This paper presents a robust and computationally efficient deep learning-based framework to recognize different real-world anomalies from the video. The proposed scheme uses a Fuzzy rule to summarize the video to scale the problem into fewer frames and the slow-fast neural network for classification. Intuitively, the designed pipeline aims to solve two significant problems that arise with video classification; one is to reduce the redundant frames and avoid the computation of optical flow for a video that has a substantial computational requirement. The proposed scheme tested on the UCF-crime dataset and has achieved recognition accuracy of 53%.