Human head detection using multi-modal object features

2004 ◽  
Author(s):  
Yun Luo ◽  
Yi Lu Murphey ◽  
F. Khairallah
Keyword(s):  
Human Head ◽  
Head Detection ◽  
Object Features

scholarly journals Head Detection Based on DR Feature Extraction Network and Mixed Dilated Convolution Module

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1565
Author(s):  
Junwen Liu ◽  
Yongjun Zhang ◽  
Jianbin Xie ◽  
Yan Wei ◽  
Zewei Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Transmission Rate ◽  
Pedestrian Detection ◽  
Human Head ◽  
Detection Rates ◽  
Translational Invariance ◽  
Dilated Convolution ◽  
Head Detection ◽  
Small Targets

Pedestrian detection for complex scenes suffers from pedestrian occlusion issues, such as occlusions between pedestrians. As well-known, compared with the variability of the human body, the shape of a human head and their shoulders changes minimally and has high stability. Therefore, head detection is an important research area in the field of pedestrian detection. The translational invariance of neural network enables us to design a deep convolutional neural network, which means that, even if the appearance and location of the target changes, it can still be recognized effectively. However, the problems of scale invariance and high miss detection rates for small targets still exist. In this paper, a feature extraction network DR-Net based on Darknet-53 is proposed to improve the information transmission rate between convolutional layers and to extract more semantic information. In addition, the MDC (mixed dilated convolution) with different sampling rates of dilated convolution is embedded to improve the detection rate of small targets. We evaluated our method on three publicly available datasets and achieved excellent results. The AP (Average Precision) value on the Brainwash dataset, HollywoodHeads dataset, and SCUT-HEAD dataset reached 92.1%, 84.8%, and 90% respectively.

Head Detection by the Adaboost Algorithm for Head Pose Estimation

2013 ◽  
Vol 427-429 ◽  
pp. 1696-1699
Author(s):  
Xiang Yang Liu ◽  
Shao Song Zhu ◽  
Su Qing Wu ◽  
Zhi Wei Shen
Keyword(s):  
Pose Estimation ◽  
Nearest Neighbor ◽  
Human Head ◽  
Head Pose Estimation ◽  
Head Tracking ◽  
Head Pose ◽  
Adaboost Algorithm ◽  
Head Detection ◽  
Dimensionality Reduction Method ◽  
Tracking Module

For human head pose analysis based on videos, pose is usually estimated on the head patch provided by a tracking module. However, head tracking is very sensitive to the large changes of pose. Therefore, this work locates the head patch in the videos by head detection. Firstly, we use the Adaboost algorithm to detect the human head in the video. Secondly, we present a dimensionality reduction method to process the head patch. Finally, we use the nearest neighbor method to estimate the head pose. The experiment results show: accurate head detecting helps to estimate the head pose. This method can be used for complex conditions of accurate head pose estimation.

scholarly journals New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5848
Author(s):  
Mohamed Chouai ◽  
Petr Dolezel ◽  
Dominik Stursa ◽  
Zdenek Nemec
Keyword(s):  
Detection System ◽  
Pedestrian Detection ◽  
Human Head ◽  
Semantic Segmentation ◽  
Person Detection ◽  
Head Detection ◽  
Bounding Boxes ◽  
Global Accuracy ◽  
Private Datasets ◽  
Safety Systems

In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.

scholarly journals DETECTION OF A HUMAN HEAD ON A LOW-QUALITY IMAGE AND ITS SOFTWARE IMPLEMENTATION

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 237-241
Author(s):  
D. Yudin ◽  
A. Ivanov ◽  
M. Shchendrygin
Keyword(s):  
Software Tool ◽  
Human Head ◽  
Recognition Algorithm ◽  
False Positives ◽  
Minimum Size ◽  
Low Light ◽  
Training Samples ◽  
Head Detection ◽  
Computer Vision Applications ◽  
And Training

<p><strong>Abstract.</strong> The paper considers the task solution of detection on two-dimensional images not only face, but head of a human regardless of the turn to the observer. Such task is also complicated by the fact that the image receiving at the input of the recognition algorithm may be noisy or captured in low light conditions. The minimum size of a person’s head in an image to be detected for is 10&amp;thinsp;&amp;times;&amp;thinsp;10 pixels. In the course of development, a dataset was prepared containing over 1000 labelled images of classrooms at BSTU n.a. V.G. Shukhov. The markup was carried out using a segmentation software tool specially developed by the authors. Three architectures of convolutional neural networks were trained for human head detection task: a fully convolutional neural network (FCN) with clustering, the Faster R-CNN architecture and the Mask R-CNN architecture. The third architecture works more than ten times slower than the first one, but it almost does not give false positives and has the precision and recall of head detection over 90% on both test and training samples. The Faster R-CNN architecture gives worse accuracy than Mask R-CNN, but it gives fewer false positives than FCN with clustering. Based on Mask R-CNN authors have developed software for human head detection on a lowquality image. It is two-level web-service with client and server modules. This software is used to detect and count people in the premises. The developed software works with IP cameras, which ensures its scalability for different practical computer vision applications.</p>

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