Object detection is a crucial branch of computer vision that aims to locate and classify
objects in images. Using deep convolutional neural networks (CNNs) as the primary …

To accurately localize occluded people in a crowd is a challenging problem in video
surveillance. Existing end-to-end deep multi-camera detectors rely heavily on pre-training …

Broiler localization is crucial for welfare monitoring particularly in identifying issues such as
wet litter. We focus on multi-camera detection systems since multiple viewpoints not only …

Multiple cameras have been frequently used to detect heavily occluded pedestrians. The
state-of-the-art methods, for deep multi-view pedestrian detection, usually project the feature …

The main challenge in multi-view pedestrian detection is integrating view-specific features
into a unified space for comprehensive end-to-end perception. Prior multi-view detection …

With the prosperity of the intelligent surveillance, multiple cameras have been applied to
localize pedestrians more accurately. However, previous methods rely on laborious …

We introduce an innovative unsupervised domain adaptation method designed to enhance
the performance of multi-view 3D pedestrian detection in unlabeled target scenes. Our …

We address multi-view pedestrian detection in a setting where labeled data is collected
using a multi-camera setup different from the one used for testing. While recent multi-view …

We unveil how generalizable AI can be used to improve multi-view 3D pedestrian detection
in unlabeled target scenes. One way to increase generalization to new scenes is to …

We address an advanced challenge of predicting pedestrian occupancy as an extension of
multi-view pedestrian detection in urban traffic. To support this, we have created a new …