无计数标注下，用检测与分类结合估算相机陷阱序列中的动物数量。
Counting Animals in Camera-Traps Image Sequences without Count Labels: Winning Solution to the iWildCam 2021 Challenge
- 基于MegaDetector检测结果，设计简单有效的计数启发式方法。
- 在无计数标签条件下，准确估算短序列中独特个体数量。
- 适合生态监测中自动化统计动物数量的场景。
相机陷阱已成为野生动物监测的重要工具，推动了从这些数据中自动提取信息的计算机视觉方法发展。尽管以往工作主要聚焦于物种识别，但许多生态应用还需估算短图像序列中出现的独特个体数量。该任务极具挑战性：相机陷阱通常以每秒约1帧的速度捕获图像序列，造成显著的时间断层，使传统多目标跟踪方法不可靠；且手动收集个体计数标注成本过高。本文描述了iWildCam 2021挑战赛的获胜方案，该挑战引入了一个在真实标注约束下（训练时无计数标注）进行序列级动物计数的基准。我们的方法MaxBoxCount结合强健的物种分类流水线与基于MegaDetector检测的简单而有效的计数启发式方法，可在无需计数标注的情况下估计独特个体数量。代码已公开于https://github.com/alcunha/iwildcam2021ufam。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。