UCF101 is introduced as the largest dataset for human action recognition, comprising 101 action classes and over 13,000 clips, totaling 27 hours of video data. The dataset includes realistic, user-uploaded videos with camera motion, cluttered backgrounds, and various lighting conditions. UCF101 addresses the limitations of existing datasets by offering a larger number of action classes and more diverse, unconstrained video environments. The dataset is divided into five types: Human-Object Interaction, Body-Motion Only, Human-Human Interaction, Playing Musical Instruments, and Sports. Baseline action recognition results using a bag of words approach yield an overall accuracy of 44.5%. The dataset is available for download and has been shown to be significantly more challenging for action recognition tasks compared to other datasets like UCF50 and HMDB51.UCF101 is introduced as the largest dataset for human action recognition, comprising 101 action classes and over 13,000 clips, totaling 27 hours of video data. The dataset includes realistic, user-uploaded videos with camera motion, cluttered backgrounds, and various lighting conditions. UCF101 addresses the limitations of existing datasets by offering a larger number of action classes and more diverse, unconstrained video environments. The dataset is divided into five types: Human-Object Interaction, Body-Motion Only, Human-Human Interaction, Playing Musical Instruments, and Sports. Baseline action recognition results using a bag of words approach yield an overall accuracy of 44.5%. The dataset is available for download and has been shown to be significantly more challenging for action recognition tasks compared to other datasets like UCF50 and HMDB51.