Classification of Tourist Photos with Gorillas and Other Animals to Detect Distancing Violations


  • Jingyu Wang Penn State University
  • Jiarui Fan
  • Ritesh Kasamsetty
  • Krittika Shahani
  • Prasenjit Mitra



implementation of machine learning in environmental topics, implementation of machine learning in animal protection, machine learning, animal protection, wildlife protection, environmental issue, computer vision, multi input model, photo classification, object detection, information science


Many people insist on getting close to wildlife without realizing how harmful the consequences of their actions can be such as the death of the animals or even themselves. To decrease the number of such events, we aim to classify tourist photos of mountain gorillas, a critically endangered species, and other animals to detect violations derived from the distance maintained between the animals and the humans. We plan to implement object detection technology to locate the human and animal targets in the image and generate features. Then, image processing techniques are applied. We propose innovative models following multi-input logic which takes in image data and non-image features with ensemble model logic to perform the classification. We show that this model performs more accurately and has a relatively shorter run-time compared to other models when detecting violations in tourist photos with animals.


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How to Cite

Wang, J., Fan, J., Kasamsetty, R. ., Shahani, K., & Mitra, P. (2023). Classification of Tourist Photos with Gorillas and Other Animals to Detect Distancing Violations. Journal of Student Research, 12(3).



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