PuTR-CouT：相机陷阱图像序列中的计数-跟踪方法

中文摘要 AI 辅助

相机陷阱图像中的物种识别已被广泛研究，但物种丰度或密度估计等关键生态建模任务还需要对单个动物进行计数。然而，大多数数据集缺乏计数标签，且帧率较低（通常约为1帧/秒），使得序列级跟踪与计数估计极具挑战性。本研究提出了PuTR-CouT，一种基于Transformer学习关联机制的计数-跟踪框架，用于相机陷阱图像中的序列级动物计数。为解决标注跟踪数据稀缺的问题，研究人员利用静态背景、短时间突发等结构先验，以弱监督方式启发式生成伪跟踪标签，构建合成训练数据。所得跟踪器关联各帧的检测结果，利用这些轨迹估计各物种的数量。研究还改进了iWildCam 2021挑战赛顶尖解决方案所用的MaxBoxCount启发式算法，将其作为强基线，取得了目前为止的最高分数。在iWildCam 2021基准上评估时，PuTR-CouT框架与改进后的MaxBoxCount相比，计数结果具有竞争力，且具备多物种预测和轨迹级验证的附加能力。

英文摘要

Species identification in camera trap images has been widely studied, but key ecological modeling tasks such as species abundance or density estimation also require counting individual animals. However, the lack of counting labels in most datasets and low frame rates (typically ~1 frame per second) make sequence-level tracking and count estimation particularly challenging. In this work, we present PuTR-CouT, a counting-by-tracking framework built on a transformer-based learned association mechanism for sequence-level animal counting in camera trap images. To address the scarcity of annotated tracking data, we generate synthetic training data by exploiting structural priors, such as static backgrounds and short temporal bursts, to heuristically create pseudo-tracking labels in a weakly supervised manner. The resulting tracker associates detections across frames, using these tracks to estimate per-species counts. We also refine the MaxBoxCount heuristic used by the top solutions of the iWildCam 2021 challenge as a strong baseline, setting the highest score reported to date. When evaluated on the iWildCam 2021 benchmark, our framework PuTR-CouT delivers competitive counting results compared to the improved MaxBoxCount, with the added capability of multi-species predictions and track-level verification.

发表机构

• Federal University of Amazonas（亚马逊联邦大学）

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