Graduate Exam Abstract

Abstract: This presentation proposes effective solutions to the real-world animal identification problem: identifying K unknown individual animals from N unlabeled camera-trap images of a given species, specifically African leopards. The research evolves from a fully automated algorithm to a human-in-the-loop framework, achieving high identification accuracy while substantially reducing human annotation effort. These approaches are particularly effective for small, unlabeled camera-trap image datasets of species that exhibit distinctive, identifiable markings and a high individual-to-image ratio, especially when many animals appear in only a single image.

Adviser: Anthony A. Maciejewski
Co-Adviser: Agnieszka Miguel
Non-ECE Member: Asa Ben-Hur, Department of Computer Science
Member 3: Pezeshki Ali, Department of Electrical & Computer Engineering
Addional Members: N/A

Publications:
[1] Cheng Guo, Agnieszka Miguel, and Anthony A. Maciejewski. Automatic identification of individual african leopards in unlabeled camera trap images. IEEE Transactions on Automation Science and Engineering, 22:2460–2471, 2025.

[2] Cheng Guo, Agnieszka Miguel, and Anthony A. Maciejewski. A human-in-the-loop solution for individual leopard identification in unlabeled camera trap images. IEEE Transactions on Automation Science and Engineering, 2025(under review).

Program of Study:
ECE455, Intro to Robot Program/Simulation
ECE555, Advanced Robotics
ECE666, Topics in Robotics
CS545, Machine Learning
CS440, Intro-Artificial Intelligence
ECE520, Optimization-Control & Communication
ECE512, Digital Signal Processing
GRAD 550, STEM Communication