Abstract
Remote cameras are an increasingly important tool for ecological research. While remote camera traps collect field data with minimal human attention, the images they collect require post-processing and characterization before it can be ecologically and statistically analyzed, requiring the input of substantial time and money from researchers. The need for post-processing is due, in part, to a high incidence of non-target images. We developed a stand-alone semi-automated computer program to aid in image processing, categorization, and data reduction by employing background subtraction and histogram rules. Unlike previous work that uses video as input, our program uses still camera trap images. The program was developed for an ungulate fence crossing project and tested against an image dataset which had been previously processed by a human operator. Our program placed images into categories representing the confidence of a particular sequence of images containing a fence crossing event. This resulted in a reduction of 54.8% of images that required further human operator characterization while retaining 72.6% of the known fence crossing events. This program can provide researchers using remote camera data the ability to reduce the time and cost required for image post-processing and characterization. Further, we discuss how this procedure might be generalized to situations not specifically related to animal use of linear features.
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Acknowledgments
We thank Alberta Parks for funding for this project and for helping with field setup and data collection and the King’s University and the King’s Centre for Visualization in Science for funding and support. This work, in part, comes from the undergraduate research projects of K. Visser and I. MacLeod. We also thank Dr. Jose Alexander and an anonymous reviewer for their comments.
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Appendices
Appendix I
Software availability: http://cs.kingsu.ca/mjanzen/CameraTrapSoftware.html.
Example instructions for running the program on a Windows computer are also available from this website.
Appendix II
Additionalsequences of images showing a false negative event, Fig. 3, and false positive event, Fig. 4. In Fig. 3, the camera trap takes its first picture after the moose has already entered the field of view, and the moose overlaps in images causing the overlap to appear as part of the background image. In Fig. 4, movement of the fence or camera causes an image change to be detected, which is retained in one of the three images. This causes the program to classify the image set into category two, implying a fast moving animal detected in only one of the three images.
An sequence of processing steps resulting in a false negative where the moose is not suitably detected
A sequence of processing steps resulting in a false positive, classifying the image set as a fast moving animal
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Janzen, M., Visser, K., Visscher, D. et al. Semi-automated camera trap image processing for the detection of ungulate fence crossing events. Environ Monit Assess 189, 527 (2017). https://doi.org/10.1007/s10661-017-6206-x
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DOI: https://doi.org/10.1007/s10661-017-6206-x