53 papers with code • 2 benchmarks • 8 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.
Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited.
Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates.
Ranked #5 on Person Re-Identification on CUHK03
In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes.
A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection.
Ranked #16 on Face Detection on WIDER Face (Hard)
Furthermore, we illustrate that diverse and dense datasets, collected by crawling the web, serve to be an efficient source of pre-training for pedestrian detection.
Ranked #1 on Pedestrian Detection on Caltech (using extra training data)
In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.
In this paper, we first explore how a state-of-the-art pedestrian detector is harmed by crowd occlusion via experimentation, providing insights into the crowd occlusion problem.
Ranked #4 on Pedestrian Detection on Caltech (using extra training data)
The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details.
Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult.