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Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference

Balasubramaniam, Sowbaranika
http://orcid.org/0009-0009-2913-9739
http://rave.ohiolink.edu/etdc/view?acc_num=osu1721417695430687

Abstract Details

2024, Master of Science, Ohio State University, Computer Science and Engineering.
Motion-activated cameras, commonly known as camera traps, play a crucial role in wildlife monitoring, biodiversity conservation, and basic ecological science, capturing a diverse range of species in their natural habitats. However, the sheer volume of images generated requires manual analysis by ecologists, making this a time-consuming and inefficient process. This is particularly frustrating since many of the images do not contain animals because camera traps are triggered by the motion of vegetation due to wind or miss the animal due to the slow camera response. This thesis presents an approach aimed at addressing these challenges through the automation of image classification and the development of smart camera traps that leverage artificial intelligence implementations in edge devices. First, we develop a species classifier pipeline consisting of object detection and a species classifier for a novel camera trap setting. We train a convolutional neural network to identify 45 trained species using 1.2M images, achieving an accuracy of around 89\% at the class level without manual intervention. This research demonstrates the combination of deep learning methodologies and human oversight, showcasing their ability to facilitate automatic or semiautomatic, precise, and efficient surveys of small animals in the wild, particularly in novel settings or for novel species. The suggested pipeline helps achieve 95\% accuracy in image classification, resulting in 11\% of images that require manual review at the class level. This pipeline helps to automatically annotate the image while maintaining accuracy. The camera trap generates a large number of images. The time delay between data capture and image processing leads to the loss of critical information. Real-time labeling while capturing images can help mitigate this issue. Following the traditional classifier, we investigate methods for deploying an AI model in a camera trap. Smart Camera Traps would result in real-time monitoring of illegal activities, detection of rare species, and removal of non-animal images. To understand the trade-off between performing computations on the edge versus the data center, we are developing a simulation environment. The development of smart camera trap technology promises to refine data collection processes, eliminate false triggers, optimize resource allocation, and improve the timeliness of data analysis, thus enhancing wildlife monitoring and research methodologies.
Tanya Berger-Wolf (Advisor)
Christopher Stewart (Committee Member)
Wei-Lun Chao (Committee Member)
Computer Engineering
Species classification; Image processing; Camera Traps; Object Detection; MegaDetector; Convolutional Neural Network; Smart Camera Traps; ICICLE; Imageomics; Tapis

Recommended Citations

Citations

  • Balasubramaniam, S. (2024). Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1721417695430687

    APA Style (7th edition)

  • Balasubramaniam, Sowbaranika. Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference. 2024. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1721417695430687.

    MLA Style (8th edition)

  • Balasubramaniam, Sowbaranika. "Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference." Master's thesis, Ohio State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=osu1721417695430687

    Chicago Manual of Style (17th edition)

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© 2024, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.