Name
Abstract | ||
|---|---|---|
Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22114094 | SENSORS |
Keywords | DocType | Volume |
animal behaviour, computerized monitoring, ectotherm, endotherm, environmental monitoring, image capture, image motion analysis, smart cameras | Journal | 22 |
Issue | ISSN | Citations |
11 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dean M Corva | 1 | 0 | 0.68 |
| Nathan I Semianiw | 2 | 0 | 0.34 |
| Anne C Eichholtzer | 3 | 0 | 0.34 |
| Scott D. Adams | 4 | 0 | 2.03 |
| M. A. Parvez Mahmud | 5 | 1 | 4.10 |
| Kendrika Gaur | 6 | 0 | 0.34 |
| Angela J L Pestell | 7 | 0 | 0.34 |
| Don A Driscoll | 8 | 0 | 0.34 |
| Abbas Z. Kouzani | 9 | 357 | 40.40 |