scholarly journals Improved Human Detection with a Fusion of Laser Scanner and Vision/Infrared Information for Mobile Applications

2018 ◽  
Vol 8 (10) ◽  
pp. 1967 ◽  
Author(s):  
Sebastian Budzan ◽  
Roman Wyżgolik ◽  
Witold Ilewicz
Keyword(s):  
Mobile Applications ◽  
Vision System ◽  
Laser Scanner ◽  
Local Binary Patterns ◽  
Detection Algorithm ◽  
Human Detection ◽  
Infrared Images ◽  
Driver Assistance Systems ◽  
Night Time ◽  
Working Distance

This paper presents a method for human detection using a laser scanner with vision or infrared images. Mobile applications require reliable and efficient methods for human detection, especially as a part of driver assistance systems, including pedestrian collision systems. The authors propose an efficient method for multimodal human detection based on a combination of the features and context information. Strictly, the human is detected in the vision/infrared images using a combination of local binary patterns and histogram of oriented gradients features with a neural network in a cascade manner. Next, using coordinates of detected humans from the vision system, the moving trajectory is predicted until the scanner working distance is reached by the individual human. Then the segmentation of data from the laser scanner is further carried out with respect to the predicted trajectory. Finally, human detection in the laser scanner working distance is performed based on modelling of the human legs. The modelling is based on the adaptive breakpoint detection algorithm and proposed improved polylines definition and fitting algorithm. The authors conducted a set of experiments in predefined scenarios, discussed the identified weakness and advantages of the proposed method, and outlined detailed future work, especially for night-time and low-light conditions.

scholarly journals CNN-Based Person Detection Using Infrared Images for Night-Time Intrusion Warning Systems

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 34 ◽  
Author(s):  
Jisoo Park ◽  
Jingdao Chen ◽  
Yong K. Cho ◽  
Dae Y. Kang ◽  
Byung J. Son
Keyword(s):  
Infrared Image ◽  
Human Detection ◽  
Detection Methods ◽  
Infrared Images ◽  
Night Time ◽  
Person Detection ◽  
Fine Grained ◽  
Public Areas ◽  
Infrared Cameras ◽  
Conventional Detection

Night-time surveillance is important for safety and security purposes. For this reason, several studies have attempted to automatically detect people intruding into restricted areas by using infrared cameras. However, detecting people from infrared CCTV (closed-circuit television) is challenging because they are usually installed in overhead locations and people only occupy small regions in the resulting image. Therefore, this study proposes an accurate and efficient method for detecting people in infrared CCTV images during the night-time. For this purpose, three different infrared image datasets were constructed; two obtained from an infrared CCTV installed on a public beach and another obtained from a forward looking infrared (FLIR) camera installed on a pedestrian bridge. Moreover, a convolution neural network (CNN)-based pixel-wise classifier for fine-grained person detection was implemented. The detection performance of the proposed method was compared against five conventional detection methods. The results demonstrate that the proposed CNN-based human detection approach outperforms conventional detection approaches in all datasets. Especially, the proposed method maintained F1 scores of above 80% in object-level detection for all datasets. By improving the performance of human detection from infrared images, we expect that this research will contribute to the safety and security of public areas during night-time.

Implementation of a Single Human Detection Algorithm for Video Digital Door Lock

2012 ◽  
Vol 19B (2) ◽  
pp. 127-134
Author(s):  
Seung-Hwan Shin ◽  
Sang-Rak Lee ◽  
Han-Go Choi
Keyword(s):  
Detection Algorithm ◽  
Human Detection ◽  
Door Lock

Based on the CO2 Gas Shielded Welding Molten Pool Image Edge Detection Algorithm

2013 ◽  
Vol 437 ◽  
pp. 840-844 ◽  
Author(s):  
Xiao Gang Liu ◽  
Bing Zhao
Keyword(s):  
Edge Detection ◽  
Molten Pool ◽  
High Speed ◽  
Vision System ◽  
Detection Algorithm ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
Defect Recognition ◽  
Pool Image

This paper use the passive vision system through high-speed camera collects molten pool images; and then according to the frequency domain characteristics of the weld pool image Butterworth low-pass filter; gradient method for image enhancement obtained after pretreatment. Research Roberts, Sobel, Prewitt, Log, Zerocross, and Canny 6 both traditional differential operator edge detection processing results. Through comparison and analysis of choosing threshold for [0.1, 0. Canny operator can get the ideal molten pool edge character, for subsequent welding molten pool defect recognition provides favorable conditions.

Fast Human Detection Algorithm Based on Subtraction Stereo for Generic Environment

2011 ◽  
Author(s):  
Alessandro Moro ◽  
Makoto Arie ◽  
Kenji Terabayashi ◽  
Kazunori Umeda ◽  
Tuan D. Pham ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Human Detection

A fast Human detection algorithm for container reach stackers

2018 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Chao Mi ◽  
◽  
Mengtong Wu ◽  
Weijian Mi ◽  
Jun Wang ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Human Detection

Human Detection Algorithm Optimization in Machine Vision

2020 ◽  
Vol 57 (10) ◽  
pp. 101006
Author(s):  
何倩倩 He Qianqian ◽  
张荣芬 Zhang Rongfen ◽  
刘宇红 Liu Yuhong
Keyword(s):  
Machine Vision ◽  
Detection Algorithm ◽  
Human Detection ◽  
Algorithm Optimization

scholarly journals Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3542 ◽  
Author(s):  
Eleftherios Lygouras ◽  
Nicholas Santavas ◽  
Anastasios Taitzoglou ◽  
Konstantinos Tarchanidis ◽  
Athanasios Mitropoulos ◽  
...  
Keyword(s):  
Embedded System ◽  
Emergency Services ◽  
Vision System ◽  
Open Water ◽  
Satellite System ◽  
Search And Rescue ◽  
Human Detection ◽  
Primary Role ◽  
Wide Range ◽  
Global Navigation Satellite

Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.

Sign in / Sign up

Export Citation Format

Share Document