Human Detection
82 papers with code • 0 benchmarks • 13 datasets
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Libraries
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Most implemented papers
RMPE: Regional Multi-person Pose Estimation
In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes.
GLTR: Statistical Detection and Visualization of Generated Text
The rapid improvement of language models has raised the specter of abuse of text generation systems.
AlignedReID++: Dynamically matching local information for person re-identification
Then, we propose a deep model name AlignedReID++ which is jointly learned with global features and local feature based on DMLI.
CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation
Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem.
MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method.
Fooling automated surveillance cameras: adversarial patches to attack person detection
Some of these approaches have also shown that these attacks are feasible in the real-world, i. e. by modifying an object and filming it with a video camera.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans
Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
Multiview Detection with Feature Perspective Transformation
First, how should we aggregate cues from the multiple views?
Search and Rescue with Airborne Optical Sectioning
We show that automated person detection under occlusion conditions can be significantly improved by combining multi-perspective images before classification.
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between human-level (global) and keypoint-level (local) information.