DPDnet proves to outperform all the evaluated methods with statistically significant differences, and with accuracies that exceed 99%, proving also to achieve high accuracy with varying datasets and experimental conditions.
Authors
D. Fuentes-Jiménez
1 papers
Roberto Martín-López
1 papers
Cristina Losada-Gutiérrez
1 papers
D. Casillas-Pérez
1 papers
Javier Macias-Guarasa
1 papers
C. Vázquez
1 papers
Daniel Pizarro-Perez
1 papers
References53 items
1
Detection of People With Camouflage Pattern Via Dense Deconvolution Network
2
UNICITY: A depth maps database for people detection in security airlocks
3
WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems
4
Convolutional Networks for Semantic Heads Segmentation using Top-View Depth Data in Crowded Environment
5
Computer vision and deep learning techniques for pedestrian detection and tracking: A survey
6
Towards Reaching Human Performance in Pedestrian Detection
7
Deep Learning using Rectified Linear Units (ReLU)
8
Parallel RCNN: A deep learning method for people detection using RGB-D images
9
Multi-Layer Proposal Network for People Counting in Crowded Scene
10
Learning and Incorporating Shape Models for Semantic Segmentation
11
Real-time human detection with depth camera via a physical radius-depth detector and a CNN descriptor
12
Robust people detection using depth information from an overhead Time-of-Flight camera
13
Detecting humans in RGB-D data with CNNs
14
Fused DNN: A Deep Neural Network Fusion Approach to Fast and Robust Pedestrian Detection
15
Xception: Deep Learning with Depthwise Separable Convolutions
16
Exploiting Depth From Single Monocular Images for Object Detection and Semantic Segmentation
17
People counting based on head detection combining Adaboost and CNN in crowded surveillance environment
18
Counting people by RGB or depth overhead cameras
19
Applications for a people detection and tracking algorithm using a time-of-flight camera
20
Human detection from images and videos: A survey
21
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
22
How Far are We from Solving Pedestrian Detection?
23
Counting pedestrians with a zenithal arrangement of depth cameras
24
Deep Residual Learning for Image Recognition
25
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
26
End-to-End People Detection in Crowded Scenes
27
You Only Look Once: Unified, Real-Time Object Detection
28
U-Net: Convolutional Networks for Biomedical Image Segmentation
29
Detecting and tracking people in real time with RGB-D camera
30
Adam: A Method for Stochastic Optimization
31
Pedestrian detection aided by deep learning semantic tasks
32
Counting people in crowded scenes by video analyzing
33
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
34
Using Time-of-Flight Measurements for Privacy-Preserving Tracking in a Smart Room
35
Joint Deep Learning for Pedestrian Detection
36
Real-Time Depth Map Based People Counting
37
A method for counting moving and stationary people by interest point classification
38
Human Tracking and Counting Using the KINECT Range Sensor Based on Adaboost and Kalman Filter
39
Reliable Human Detection and Tracking in Top-View Depth Images
40
People Detection and Tracking from a Top-View Position Using a Time-of-Flight Camera
41
ImageNet classification with deep convolutional neural networks
42
Robust people counting system based on sensor fusion
43
Water Filling: Unsupervised People Counting via Vertical Kinect Sensor
44
A People Counting System Based on Face-Detection
45
K-means based segmentation for real-time zenithal people counting
46
Privacy preserving crowd monitoring: Counting people without people models or tracking
47
People-tracking-by-detection and people-detection-by-tracking
48
Tracking People by Learning Their Appearance
49
People Tracking Using a Time-of-Flight Depth Sensor
50
The GEINTRA Overhead ToF People Detection (GOTPD1) database
51
Robust People Detection and Tracking from an Overhead Time-of-Flight Camera
52
RGB-D Human Detection and Tracking for Industrial Environments
53
The MIVIA People Counting Dataset. Available online