发表机构
Federal University of Amazonas（亚马逊联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出MaxBoxCount方法，结合物种分类流水线与MegaDetector检测的计数启发式算法，在无计数标注的情况下完成动物计数，为iWildCam 2021挑战赛的获胜方案。
AI 中文摘要
相机陷阱已成为野生动物监测的重要工具，推动了用于从这些数据中自动提取信息的计算机视觉方法的发展。尽管大多数现有研究聚焦于物种识别，但许多生态应用还需要估算在短图像序列中出现的独特个体数量。该任务极具挑战性，原因在于相机陷阱通常以约每秒1帧的速率拍摄图像序列，产生的时间不连续性可能使传统多目标跟踪方法不可靠，且手动收集个体计数标注的成本过高。本研究描述了iWildCam 2021挑战赛的获胜解决方案，该挑战赛在训练时无计数标注的现实标注约束下，提供了序列级动物计数的基准。我们的方法MaxBoxCount将强大的物种分类流水线与基于MegaDetector检测的简单却有效的计数启发式算法相结合，无需计数标注即可估算独特个体数量。代码可在指定URL获取。
英文摘要
Camera traps have become an essential tool for wildlife monitoring, motivating the development of computer vision methods for the automated extraction of information from these data. While most prior work has focused on species identification, many ecological applications also require estimating the number of unique individuals appearing across short image sequences. This task is particularly challenging because camera traps typically acquire bursts of images at approximately one frame per second, creating large temporal discontinuities that may make conventional multi-object tracking methods unreliable, and because manually collecting individual count annotations is prohibitively expensive. In this work, we describe the winning solution to the iWildCam 2021 Challenge, which introduced a benchmark for counting animals at the sequence level under realistic annotation constraints where count annotations are unavailable for training. Our approach, MaxBoxCount, combines a strong species classification pipeline with a simple yet effective counting heuristic based on MegaDetector detections to estimate the number of unique individuals without requiring count annotations. Code is available at https://github.com/alcunha/iwildcam2021ufam.
