A Hybrid Approach to Tiger Re-Identification
Published in ICCV 2019 Workshop (Computer Vision for Wildlife Conservation), 2019
Visual data analytics is increasingly becoming an important part of wildlife monitoring and conservation strategies. In this work, we discuss our solution to the image-based Amur tiger re-identification (Re-ID) challenge hosted by the CVWC Workshop at ICCV 2019. Various factors like poor quality images, lighting and pose variations, and limited images per identity make tiger Re-ID a difficult task for deep learning models. Consequently, we propose to utilize both deep learning and traditional SIFT descriptor-based matching for tiger re-identification.