Three-dimensional measurements of animal paths using handheld unconstrained GoPro cameras and VSLAM software

Recording animal-view videos of the natural world using a novel camera system and software package

Plants, animals, and fungi display a rich tapestry of colors. Animals, in particular, use colors in dynamic displays performed in spatially complex environments. Although current approaches for studying colors are objective and repeatable, they miss the temporal variation of color signals entirely. Here, we introduce hardware and software that provide ecologists and filmmakers the ability to accurately record animal-perceived colors in motion. Specifically, our Python codes transform photos or videos into perceivable units (quantum catches) for animals of known photoreceptor sensitivity. The plans and codes necessary for end-users to capture animal-view videos are all open source and publicly available to encourage continual community development. The camera system and the associated software package will allow ecologists to investigate how animals use colors in dynamic behavioral displays, the ways natural illumination alters perceived colors, and other questions that remained unaddressed until now due to a lack of suitable tools. Finally, it provides scientists and filmmakers with a new, empirically grounded approach for depicting the perceptual worlds of nonhuman animals.

Review of methods for animal videography using camera systems that automatically move to follow the animal

Digital photography and videography provide rich data for the study of animal behavior and are consequently widely used techniques. For fixed, unmoving cameras, the image sensor and optics govern the field of view and spatial detail. For a given sensor resolution, the optics determine a tradeoff between high magnification in which high spatial detail from a restricted field of view is obtained versus low magnification in which lower spatial detail is obtained from a larger region. In addition to this geometric resolution versus field of view tradeoff, limited light availability establishes a physical limit when imaging movement. If the animal is moving, motion blur smears the subject on the sensor during exposure. Practically, motion blur is further compounded by sensor inefficiency and noise. While these fundamental tradeoffs with stationary cameras can be sidestepped by employing multiple cameras and providing additional illumination, this may not always be desirable. An alternative that overcomes these issues of stationary cameras is to direct a high magnification camera at an animal continually as it moves. Here we review systems in which automatic tracking is used to maintain an animal in the working volume of a moving optical path. Such methods provide an opportunity to escape the tradeoff between resolution and field of view and also to reduce motion blur while still enabling automated image acquisition. We argue that further development will be useful and outline potential innovations that may improve the technology and lead to more widespread use.