Human Detection
82 papers with code • 0 benchmarks • 13 datasets
Benchmarks
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Libraries
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Most implemented papers
Scale Match for Tiny Person Detection
In this paper, we introduce a new benchmark, referred to as TinyPerson, opening up a promising directionfor tiny object detection in a long distance and with mas-sive backgrounds.
DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data
Detecting persons using a 2D LiDAR is a challenging task due to the low information content of 2D range data.
Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
From the experimental analysis, it is observed that the YOLO v3 with Deepsort tracking scheme displayed best results with balanced mAP and FPS score to monitor the social distancing in real-time.
DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera
In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability.
RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text
In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text.
SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images
With the increasing demand for search and rescue, it is highly demanded to detect objects of interest in large-scale images captured by Unmanned Aerial Vehicles (UAVs), which is quite challenging due to extremely small scales of objects.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Advances in Large Language Models (e. g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.
Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation
State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e. g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose, hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR.
Context-aware CNNs for person head detection
First, we leverage person-scene relations and propose a Global CNN model trained to predict positions and scales of heads directly from the full image.
Interspecies Knowledge Transfer for Facial Keypoint Detection
Instead of directly finetuning a network trained to detect keypoints on human faces to animal faces (which is sub-optimal since human and animal faces can look quite different), we propose to first adapt the animal images to the pre-trained human detection network by correcting for the differences in animal and human face shape.