Lifting Multi-View Detection and Tracking to the Bird's Eye View
Torben Teepe, Philipp Wolters, Johannes Gilg, Fabian Herzog, Gerhard, Rigoll

TL;DR
This paper enhances multi-view detection and tracking by aggregating multi-view features onto a bird's eye view, integrating appearance and motion cues, and generalizing across multiple datasets for improved performance.
Contribution
It introduces a novel architecture that combines multi-view feature aggregation with multi-step learning and cross-domain generalization for detection and tracking.
Findings
Achieves state-of-the-art results on multiple datasets.
Effectively combines appearance and motion cues.
Generalizes across pedestrian and roadside perception domains.
Abstract
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…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Vision and Imaging · Robotics and Sensor-Based Localization
MethodsFocus
