100 papers with code • 6 benchmarks • 15 datasets
Pedestrian detection is the task of detecting pedestrians from a camera.
Further state-of-the-art results (e.g. on the KITTI dataset) can be found at 3D Object Detection.
By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation.
The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details.
We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes.
We discuss the challenges of creating such a framework, collecting existing and new data, gathering state-of-the-art methods to be tested on the datasets, and finally creating a unified evaluation system.
Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates.