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。

将多视角检测和跟踪提升至鸟瞰视角

Lifting Multi-View Detection and Tracking to the Bird's Eye View

Multi-view aggregation promises to overcome the occlusion and missed
detection challenge in multi-object detection and tracking. Recent approaches
in multi-view detection and 3D object detection made a huge performance leap by
projecting all views to the ground plane and performing the detection in the
Bird's Eye View (BEV). In this paper, we investigate if tracking in the BEV can
also bring the next performance breakthrough in Multi-Target Multi-Camera
(MTMC) tracking. Most current approaches in multi-view tracking perform the
detection and tracking task in each view and use graph-based approaches to
perform the association of the pedestrian across each view. This spatial
association is already solved by detecting each pedestrian once in the BEV,
leaving only the problem of temporal association. For the temporal association,
we show how to learn strong Re-Identification (re-ID) features for each
detection. The results show that early-fusion in the BEV achieves high accuracy
for both detection and tracking. EarlyBird outperforms the state-of-the-art
methods and improves the current state-of-the-art on Wildtrack by +4.6 MOTA and
+5.6 IDF1.

多视图聚合承诺克服多目标检测和跟踪中的遮挡和漏检问题，在传统的横视图检测方法基础上，本文探索了在鸟瞰视图中进行跟踪的性能突破，通过在鸟瞰视图中进行早期融合，学习强大的重新识别特征，提高了检测和跟踪的准确性。