Person attribute recognition (PAR) is crucial for city surveillance as it enables the identification and tracking of individuals across multiple cameras. It also gives the system the ability to retrieve instances that have specific attributes, a crucial requirement in surveillance applications. Typical attributes include gender, clothing style, carrying items, etc, which provide high-level semantic information. Existing standard datasets like PETA, Market 1501, and PA100K lack attribute classes relevant to Indian attire, such as kurta, salwar, dupatta, and saree, etc, which are essential for the Indian context. Several solutions are also available in the literature which gives good results on these datasets. But these solutions are not directly applicable to the Indian scenario where there is a change in skin color, dressing style, etc. Even the class information needs to be modified to suit the Indian scenario. The proposed challenge addresses this gap by focusing on detecting person attributes explicitly tailored for the Indian scenario, enhancing the accuracy and relevance of attribute recognition in smart city environments. We will be providing a sample dataset with the intent of sensitizing the participants of the Indian scenario. Participants are encouraged to enhance the dataset to meet their training requirements in a suitable manner.