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

Face detection has drawn much attention in recent decades since the seminal
work by Viola and Jones. While many subsequences have improved the work with
more powerful learning algorithms, the feature representation used for face
detection still can't meet the demand for effectively and efficiently handling
faces with large appearance variance in the wild. To solve this bottleneck, we
borrow the concept of channel features to the face detection domain, which
extends the image channel to diverse types like gradient magnitude and oriented
gradient histograms and therefore encodes rich information in a simple form. We
adopt a novel variant called aggregate channel features, make a full
exploration of feature design, and discover a multi-scale version of features
with better performance. To deal with poses of faces in the wild, we propose a
multi-view detection approach featuring score re-ranking and detection
adjustment. Following the learning pipelines in Viola-Jones framework, the
multi-view face detector using aggregate channel features shows competitive
performance against state-of-the-art algorithms on AFW and FDDB testsets, while
runs at 42 FPS on VGA images.

利用聚合通道特征的多视角检测方法提高人脸检测的性能， 能够处理野外环境中不同表现形式的人脸。