Species Detection app workflow: The collected video/image feeds are processed to extract the relevant frames. Deep learning AI models are then trained to draw bounding boxes around each species/animal passing in front of the camera. The entire workflow, encapsulated in a web app, automates the process of video/image feed input, detection and classification. The automated AI solution leverages the latest deep learning algorithms using Microsoft Azure and Cognitive Services platform stack. This is a cross browser web app that can take video/images of animals as input and classify them into different species (macaques/monkeys, lesser mousedeer & wild pig). It returns both image and video output (JSON string which contains a base64 encoded string photo) with the species detected and specific species identified. It returns metrics such as confidence score and vertices for bounding boxes of species detected. To achieve higher accuracy, we recommend properly exposed, unobstructed JPEG/PNG photos at a min. of 640 x 480 resolution. The animals/species must be in focus and unobstructed, to be clearly visible in the images. Also, the quality and the angles of the camera are important as they contribute to a higher reading accuracy. For a quick trial, try this demo app (https://gramener.com/cvdemo/species.tmpl.html) Users of this solution could be for profit or not for profit organizations (animal conservationists, zoos, etc.) aiming to provide social, economic, and environmental benefits through effective conservation, management, promotion, and development of the species. You own the commercial copyright of the resulting JSON with no additional fee, meaning you may use it for commercial purposes Please contact reachus@gramener.com for pricing or any other details.