Testing the detection of large, secretive snakes using camera traps

Authors: Danielle K. Walkup, Wade A. Ryberg, Josh B. Pierce, Emlyn Smith, James Childress, Forrest East, Brian L. Pierce, Price Brown, Corey M. Fielder, Toby J. Hibbitts

Novel technologies, such as camera traps, have expanded the opportunities for species detection, especially for rare species. Corresponding changes in data processing must occur to handle the large volume of data gathered from technology like camera traps. Automated image data processing, usually by running images through different types of computer algorithms, is an overarching goal to reduce the number of images that researchers must manually review. However, differences in camera trap setups and species characteristics can make automatic processing a challenge. Here, we evaluated the detection accuracy and efficiency of a time‐lapse triggered camera trapping technique combined with a pixel change detection algorithm as part of a monitoring program for a translocated population of the rare and federally threatened Louisiana Pinesnake (Pituophis ruthveni). We paired 5 cameras with automated pit tag readers to collect observations of P. ruthveni. We evaluated an image dataset of 1,500,187 images, collected over 7 months, both manually (i.e., researchers looking at each individual picture to determine snake
presence) and automatically using a change detection algorithm. There were 18 P. ruthveni observations recorded by the tag readers, 7 of which occurred while a paired camera was not operational. Ten of the tag reader P. ruthveni observations were captured by the paired camera trap, with an additional P. ruthveni observation from a paired camera trap not recorded by the tag reader. There were 132 snake observations of 13 additional species and 18 observations of unknown snakes from the camera traps. The algorithm reduced the number of images reviewers evaluated by an average of 78.5% per camera (range = 37.3–98.7%) but had a 54.5% success rate at detecting observations of P. ruthveni (47.1% for individual images), and a slightly lower 48.9% success rate detecting other large snakes. Large snakes were 4 times more likely to be flagged by the algorithm than small snakes. Our time‐lapse triggered camera trapping technique performed well with respect to P. ruthveni detection accuracy, compared to the tag readers. However, further research is needed to improve quality assurances of camera trap image filtering and object recognition algorithms across different sites or environments.

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