LAUSR

Human–Animal Conflict (HAC) is one of the primary threats to the continued survival of animal species and it has also impacted the lives of humans drastically. In this paper, we propose an efficient animal detection and recognition system with invariant features and fuzzy logic using thermal images. The proposed system exploits various features like Zernike, shape, texture and skeleton path. Cumulatively, these features are invariant to rotation, scaling, translation, illumination, and partly posture. The proposed model is robust to several challenging image conditions like low contrast/illumination, haze/blur, occlusion, camouflage, background clutter, and poses variation. The model is tested on our thermal animal dataset that has 1862 images and 12 different animal species. Experimental results validate the significance of thermal images for animal-based applications. Besides, the proposed fuzzy system has achieved an average accuracy of 97% which is equivalent to the accuracy produced by domain experts in identifying the animals from our thermal dataset.