Animal Detection in Man-made Environments

Abhineet Singh, Marcin Pietrasik, Gabriell Natha, Nehla Ghouaiel, Ken Brizel, Nilanjan Ray; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1438-1449

Abstract


Automatic detection of animals that have strayed into human inhabited areas has important security and road safety applications. This paper attempts to solve this problem using deep learning techniques from a variety of computer vision fields including object detection, tracking, segmentation and edge detection. Several interesting insights into transfer learning are elicited while adapting models trained on benchmark datasets for real world deployment. Empirical evidence is presented to demonstrate the inability of detectors to generalize from training images of animals in their natural habitats to deployment scenarios of man-made environments. A solution is also proposed using semi-automated synthetic data generation for domain specific training. Code and data used in the experiments are made available to facilitate further work in this domain.

Related Material


[pdf]
[bibtex]
@InProceedings{Singh_2020_WACV,
author = {Singh, Abhineet and Pietrasik, Marcin and Natha, Gabriell and Ghouaiel, Nehla and Brizel, Ken and Ray, Nilanjan},
title = {Animal Detection in Man-made Environments},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}