Abstract
Decreasing costs in high‐quality digital cameras, image processing, and digital storage allow researchers to generate and store massive amounts of digital imagery. The time needed to manually analyze these images will always be a limiting factor for experimental design and analysis. Implementation of computer vision algorithms for automating the detection and counting of animals reduces the manpower needed to analyze field images.For this paper, we assess the ability of computer vision to detect and count birds in images from a field test that was not designed for computer vision. Using video stills from the field test and Matlab's Computer Vision Toolbox, we designed and evaluated a cascade object detection method employing Haar and Local Binary Pattern feature types.Without editing the images, we found that the Haar feature can have a recall over 0.5 with an Intersection over Union threshold of 0.5. However, using this feature, 86% of the frames without birds had false‐positive bird detections. Reducing the false positives could lead to these detection methods being implemented into a fully automated system for detecting and counting birds.Accurately detecting and counting birds using computer vision will reduce manpower for field experiments, both in experimental design and data analysis. Improvements in automated detection and counting will allow researchers to design extended trials without the added step of optimizing the experimental setup and/or captured images for computer vision.
Highlights
This paper explores using computer vision for automated detection and counting of birds from video captured in a field study that was not designed to use computer vision
We assess the ability of computer vision to detect and count birds in images from a field test that was not designed for computer vision
We focused on the effectiveness of using a cascade object detector for automated bird detection to determine the presence of birds on food tables
Summary
This paper explores using computer vision for automated detection and counting of birds from video captured in a field study that was not designed to use computer vision. Examples of classification algorithms commonly used in biological applications are artificial neural networks, support vector machines, and cascade object detectors (Cheng & Han, 2016; Stallkamp, Schlipsing, Salmen, & Igel, 2012) Detecting animals in their natural environment, in most cases, is trivial for humans, but the finite attention span of human investi‐ gators will always limit the amount of imagery that can be analyzed. Optimizing computer automated detection for real‐world con‐ ditions will reduce manpower needed for data analysis in a wide variety of useful experiments To demonstrate this possibility, we trained a computer automated detection method to detect and count birds in video frames from a field test intended to be ana‐ lyzed by humans. Implementing an accurate bird detection algorithm will allow real‐ time automated bird counting to be incorporated in a wide range of future projects, saving time, and manpower
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