论文标题
在人群中具有拓扑限制
Localization in the Crowd with Topological Constraints
论文作者
论文摘要
我们解决了人群本地化的问题，即对与拥挤的场景中人们相对应的点的预测。由于各种挑战，一种定位方法容易出现空间语义错误，即预测同一人内的多个点或在混乱区域中崩溃多个点。我们提出了针对这些语义错误的拓扑方法。我们介绍了一种拓扑约束，该拓扑约束教会模型来推理点的空间布置。为了实施这一约束，我们根据持续的同源性理论来定义持久性损失。损失比较了似然图的地形格局和地面真相的拓扑。拓扑推理提高了本地化算法的质量，尤其是在混乱的区域附近。在多个公共基准测试中，我们的方法的表现优于先前的本地化方法。此外，我们证明了方法在改善人群计数任务中的性能方面的潜力。
We address the problem of crowd localization, i.e., the prediction of dots corresponding to people in a crowded scene. Due to various challenges, a localization method is prone to spatial semantic errors, i.e., predicting multiple dots within a same person or collapsing multiple dots in a cluttered region. We propose a topological approach targeting these semantic errors. We introduce a topological constraint that teaches the model to reason about the spatial arrangement of dots. To enforce this constraint, we define a persistence loss based on the theory of persistent homology. The loss compares the topographic landscape of the likelihood map and the topology of the ground truth. Topological reasoning improves the quality of the localization algorithm especially near cluttered regions. On multiple public benchmarks, our method outperforms previous localization methods. Additionally, we demonstrate the potential of our method in improving the performance in the crowd counting task.
