Abstract
Nowadays, camera traps are widely employed in monitoring biodiversity and assessing the population density of animal species. A challenge in animal recognition in camera trap images is the detection of small animals in complex environments and the identification of heavily obscured animals. This paper presents two novel methods that leverage sequentially captured images to improve animal recognition accuracy: one utilizing optical flow information and the other a motion-based algorithm based on the principle of median filtering. In experiments, we used two new real-world sequence-based camera trap image datasets to evaluate these methods. Our findings indicate that optical flow information effectively reduces false positive cases, while the motion-based algorithm significantly improves the accuracy of detecting animal presence and counting by substantially reducing false negative cases. Specifically, using the MegaDetector with a confidence threshold of 0.5 as the baseline, the motion-based method reduced false negative cases by over 70% while only slightly increasing false positive cases, and improved animal counting accuracy by more than 25%.