Pedestrian Detection

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.

( Image credit: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection )

Libraries

Use these libraries to find Pedestrian Detection models and implementations
3 papers
19,739
2 papers
11,647
See all 6 libraries.

Most implemented papers

Focal Loss for Dense Object Detection

facebookresearch/detectron ICCV 2017

Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

Feature Pyramid Networks for Object Detection

facebookresearch/detectron CVPR 2017

Feature pyramids are a basic component in recognition systems for detecting objects at different scales.

FCOS: Fully Convolutional One-Stage Object Detection

tianzhi0549/FCOS ICCV 2019

By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.

Fast Algorithms for Convolutional Neural Networks

XiaoMi/mace CVPR 2016

The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes.

Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

yueruchen/Pedestrian-Synthesis-GAN 5 Apr 2018

The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details.

Detection in Crowded Scenes: One Proposal, Multiple Predictions

megvii-model/CrowdDetection CVPR 2020

We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes.

MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking

khalidw/MOT16_Annotator 8 Apr 2015

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.

Joint Detection and Identification Feature Learning for Person Search

ShuangLI59/person_search CVPR 2017

Existing person re-identification benchmarks and methods mainly focus on matching cropped pedestrian images between queries and candidates.

Learning Cross-Modal Deep Representations for Robust Pedestrian Detection

SoonminHwang/rgbt-ped-detection CVPR 2017

Then, the learned feature representations are transferred to a second deep network, which receives as input an RGB image and outputs the detection results.

Accurate Single Stage Detector Using Recurrent Rolling Convolution

xiaohaoChen/rrc_detection CVPR 2017

In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.