Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos^†

Human Action Recognition is considered to be a critical problem and it is always a challenging issue in computer vision applications, especially video surveillance applications. State-of-the-art classifiers introduced to solve the problem are computationally expensive to train and require very large amounts of data. In this paper, we solve the problems of low data and resource availability in surveillance datasets by employing transfer learning and fine-tuning the Inflated 3D CNN model and the SlowFast Network model to automatically extract features from surveillance videos in the SPHAR dataset for classification into respective action classes. This approach works well to process the spatio-temporal nature of videos. Fine-tuning is carried out in the networks by replacing the last classification (dense) layer as per the available number of classes in the constructed new dataset. We ultimately compare the performance of both fine-tuned networks by taking accuracy as the metric, and find that the I3D model performs better for our use-case.