Video Classification using SlowFast Network via Fuzzy rule

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%.