  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. https://github.com/tteepe/TrackTacular
