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Automatically understanding and describing the visual content of videos in natural language is a challenging task in computer vision. Existing approaches are often designed to describe single events in a closed-set setting. However, in real-world scenarios, concurrent activities and previously unseen actions may appear in a video. This work presents the OSVidCap, a novel open-set video captioning framework that recognizes and describes, in natural language, concurrent known actions and deal with unknown ones. The OSVidCap is based on the encoder-decoder framework and uses a detection-and-tracking-object-based mechanism followed by a background blurring method to focus on specific targets in a video. Additionally, we employ the TI3D Network with the Extreme Value Machine (EVM), which learns representations and recognizes unknown actions. We evaluate the proposed approach on the benchmark ActivityNet Captions dataset. Also, an enhanced version of the LIRIS human activity dataset was proposed by providing descriptions for each action. We also provide spatial, temporal, and caption annotations for existing unlabeled actions in the dataset - considered unknown actions in our experiments. Experimental results showed our method&#x2019;s effectiveness in recognizing and describing concurrent actions in natural language and the strong ability to deal with detected unknown activities. Based on these results, we believe that the proposed approach can be potentially helpful for many real-world applications, including human behavior analysis, safety monitoring, and surveillance.

OSVidCap: A Framework for the Simultaneous Recognition and Description of Concurrent Actions in Videos in an Open-Set Scenario