Accurate action recognition in cooking videos holds significant importance for various applications, such as recipe recommendation systems, dietary monitoring, and interactive cooking tutorials. In this research, we introduce the CookingINWild dataset, a novel collection of diverse Indian cuisine cooking videos sourced from YouTube, capturing real-world, uncontrolled scenarios. We tested our dataset with advanced action recognition models that usually work well. Surprisingly, they didn't do as great on our dataset because Indian cooking actions are complex and our videos aren't controlled. This shows we need special methods for these challenges. Our work connects controlled and real-world situations, making room for better action recognition in Indian cooking videos. The CookingINWild dataset represents a valuable resource for exploring Indian cooking techniques, encouraging the researchers to address these distinctive challenges and enhance action recognition in Indian cooking videos.
