76 papers with code • 6 benchmarks • 12 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.
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.
Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results.
In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.