Recognizing what is happening in a movie is an essential aspect of various software, such as video analysis, video surveillance, and human-computer interaction. However, the presence of out-of-distribution (OOD) samples can often hamper the performance of action recognition systems. The purpose of this work is to develop a system that can detect human actions in video sequences along with detecting OOD samples. We propose a simple model that uses I3D features to identify the actions in the video. To detect the unseen (OOD) samples we analyzed various loss-functions that drops the confidence of model more on the unseen samples as compared to seen samples. We evaluated our system’s performance on the UCF101 dataset. The results show that proposed model detect seen actions with 91% whereas unseen actions with 80% accuracy. This research opens up new possibilities for creating powerful action recognition systems that can operate effectively in real-world environments.