Pedestrian Detection
113 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.
( Image credit: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection )
Libraries
Use these libraries to find Pedestrian Detection models and implementationsDatasets
Most implemented papers
Focal Loss for Dense Object Detection
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.
FCOS: Fully Convolutional One-Stage 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.
Feature Pyramid Networks for Object Detection
Feature pyramids are a basic component in recognition systems for detecting objects at different scales.
Fast Algorithms for Convolutional Neural Networks
The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes.
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.
Detection in Crowded Scenes: One Proposal, Multiple Predictions
We propose a simple yet effective proposal-based object detector, aiming at detecting highly-overlapped instances in crowded scenes.
Multiview Detection with Feature Perspective Transformation
First, how should we aggregate cues from the multiple views?
Exploring Visual Context for Weakly Supervised Person Search
This paper inventively considers weakly supervised person search with only bounding box annotations.
Why do linear SVMs trained on HOG features perform so well?
Linear Support Vector Machines trained on HOG features are now a de facto standard across many visual perception tasks.
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
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.