TY - JOUR AU - Abousamra, Shahira AU - Hoai, Minh AU - Samaras, Dimitris AU - Chen, Chao PY - 2021/05/18 Y2 - 2024/03/28 TI - Localization in the Crowd with Topological Constraints JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 2 SE - AAAI Technical Track on Computer Vision I DO - 10.1609/aaai.v35i2.16170 UR - https://ojs.aaai.org/index.php/AAAI/article/view/16170 SP - 872-881 AB - 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. ER -