Taking advantage of multi-view aggregation presents a promising solution to
tackle challenges such as occlusion and missed detection in multi-object
tracking and detection. Recent advancements in multi-view detection and 3D
object recognition have significantly improved performance by strategically
projecting all views onto the ground plane and conducting detection analysis
from a Bird's Eye View. In this paper, we compare modern lifting methods, both
parameter-free and parameterized, to multi-view aggregation. Additionally, we
present an architecture that aggregates the features of multiple times steps to
learn robust detection and combines appearance- and motion-based cues for
tracking. Most current tracking approaches either focus on pedestrians or
vehicles. In our work, we combine both branches and add new challenges to
multi-view detection with cross-scene setups. Our method generalizes to three
public datasets across two domains: (1) pedestrian: Wildtrack and MultiviewX,
and (2) roadside perception: Synthehicle, achieving state-of-the-art
performance in detection and tracking. this https URL

利用多视角聚合的优势可以解决多目标跟踪和检测中的遮挡和漏检等挑战。通过将所有视图投影到地面并从鸟瞰视图进行检测分析，最近在多视图检测和 3D 物体识别方面的进展显著提高了性能。本文比较现代的无参数和参数化的升降方法与多视角聚合方法，并提出一种结合多个时间步骤的特征聚合以学习稳健检测并结合外观和运动线索进行跟踪的架构。我们的方法在行人和道路感知两个领域的三个公共数据集上具有广泛适用性，并在检测和跟踪方面实现了最先进的性能。