With the growing advancement of artificial intelligence, its scope can also be expanded towards wildlife protection without harming the ecological balance that is caused with the use of invasive techniques. These invasive techniques include fitting of trackers, injecting biochemical transmitters, etc., which not only hinders the physical ecology of the animals but also requires manual searching and fitting the devices. Hence, non-invasive techniques have proved to be very useful in this scenario. The proposed work aims to use non-invasive techniques to identify endangered animals in the wild using YOLOv2 object detection algorithm. Since animal detection in the wild is a challenging task, therefore the captured videos for tracking are converted into a series of image sequences where the detection algorithm is applied. A surveillance system on a smaller scale is proposed for three specific endangered animals based on the detection model that detects and monitors the animals on a real-time basis. The work is carried out for three specific endangered animals, i.e. tigers, elephants, and one-horned rhinos. Since elephants and rhinos can appear similar in terms of skin colour depending on the environmental changes, blob analysis is performed as a support study to resolve this problem. However, it has been noticed that increasing the data also removes this error while using YOLOv2. The model is robust to distortions and unwanted point of references.