Abstract
Animal tracking and pose estimation systems, such as STEP (Simultaneous Tracking and Pose Estimation) and ViTPose, experience substantial performance drops when processing images and videos with cage structures and systematic occlusions. We present a three-stage preprocessing pipeline that addresses this limitation through: (1) cage segmentation using a Gabor-enhanced ResNet-UNet architecture with tunable orientation filters, (2) cage inpainting using CRFill for content-aware reconstruction of occluded regions, and (3) evaluation of pose estimation and tracking on the uncaged frames. Our Gabor-enhanced segmentation model leverages orientation-aware features with 72 directional kernels to accurately identify and segment cage structures that severely impair the performance of existing methods. Experimental validation demonstrates that removing cage occlusions through our pipeline enables pose estimation and tracking performance comparable to that in environments without occlusions. We also observe significant improvements in keypoint detection accuracy and trajectory consistency.
Abstract (translated)
动物跟踪和姿态估计系统（如STEP（同时跟踪与姿态估计）和ViTPose）在处理含有笼子结构和系统性遮挡的图像和视频时，性能会显著下降。我们提出了一种三阶段预处理流水线来解决这一问题： 1. 使用改进了Gabor滤波器的ResNet-UNet架构进行笼子分割，该模型具有可调方向滤波器； 2. 采用CRFill技术对遮挡区域进行内容感知重建（即“填充”）以恢复被笼子遮挡的部分； 3. 在去除了笼子后的图像帧上评估姿态估计和跟踪性能。 我们的Gabor增强型分割模型利用了具有72个方向内核的方向感知特征，能够准确地识别并分割严重妨碍现有方法性能的笼子结构。实验验证表明，通过我们的流水线去除遮挡后，姿态估计和跟踪性能可与无遮挡环境中的表现相媲美，并且在关键点检测精度和轨迹一致性方面取得了显著改进。
URL
https://arxiv.org/abs/2512.07712
