  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.
