Grouping behavior of wild camel (Camelus ferus) referred from video data of camera trap in Kumtag Desert

2014 ◽  
Vol 22 (6) ◽  
pp. 746 ◽  
Author(s):  
Xue Yadong ◽  
Liu Fang ◽  
Zhang Yuguang ◽  
Li Diqiang
Keyword(s):  
Camera Trap ◽  
Video Data ◽  
Grouping Behavior ◽  
Camelus Ferus

scholarly journals A practical guide for estimating animal density using camera traps: Focus on the REST model

2021 ◽  
Author(s):  
Shun Hongo ◽  
Yoshihiro Nakashima ◽  
Gota Yajima
Keyword(s):  
Wildlife Conservation ◽  
Camera Trap ◽  
Camera Traps ◽  
Video Data ◽  
Camera Placement ◽  
Practical Guide ◽  
Animal Density ◽  
Forested Areas ◽  
Staying Time

Estimating animal density and finding the factors that influence it are central in wildlife conservation and management but challenging to achieve, particularly in forested areas. Camera trapping is a pervasive method in forest mammal survey and a plausible technique to overcome this challenge. This report provides a practical guide for conducting a camera trap survey to estimate the density of forest mammals applying the random encounter and staying time (REST) model. We firstly provide a brief explanation about the structure and assumptions of the REST model. Next, we describe essential points during different steps in planning a survey: determination of objectives, design of camera placement, choice of camera models, camera setting, the layout of the camera station, and list of covariates. We then develop detail-oriented instruction for conducting a survey and analysing the obtained video data. We encourage camera trap surveyors to provide the practised protocols of their surveys, which will be helpful to other camera trappers.

scholarly journals Comparing Methods for Assessing Chimpanzee (Pan troglodytes schweinfurthii) Party Size: Observations, Camera Traps, and Bed Counts from a Savanna–Woodland Mosaic in the Issa Valley, Tanzania

2020 ◽  
Vol 41 (6) ◽  
pp. 901-915 ◽  
Author(s):  
Daphne N. Vink ◽  
Fiona A. Stewart ◽  
Alex K. Piel
Keyword(s):  
Pan Troglodytes ◽  
Camera Trap ◽  
Camera Traps ◽  
Primate Behavior ◽  
Wet Season ◽  
Party Size ◽  
Direct Observations ◽  
Future Work ◽  
Grouping Behavior ◽  
Size Estimates

AbstractStudying animal grouping behavior is important for understanding the causes and consequences of sociality and has implications for conservation. Chimpanzee (Pan troglodytes) party size is often assessed by counting individuals or extracted indirectly from camera trap footage or the number of nests. Little is known, however, about consistency across methods for estimating party size. We collected party size data for wild chimpanzees in the Issa valley, western Tanzania, using direct observations, camera traps, and nest counts over six years (2012–2018). We compared mean monthly party size estimates calculated using each method and found that estimates derived from direct observations were weakly positively correlated with those derived from camera traps. Estimates from nest counts were not significantly correlated with either direct observations or camera traps. Overall observed party size was significantly larger than that estimated from both camera traps and nest counts. In both the dry and wet seasons, observed party size was significantly larger than camera trap party size, but not significantly larger than nest party size. Finally, overall party size and wet season party size estimated from camera traps were significantly smaller than nest party size, but this was not the case in the dry season. Our results reveal how data collection methods influence party size estimates in unhabituated chimpanzees and have implications for comparative analysis within and across primate communities. Specifically, future work must consider how estimates were calculated before we can reliably investigate environmental influences on primate behavior.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

APPLICATION OF THE EARLY DAMAGE DIAGNOSTICS TECHNIQUE TO EXAMINATION OF THE AVIATION PANEL

2019 ◽  
Vol 85 (6) ◽  
pp. 53-63 ◽  
Author(s):  
I. E. Vasil’ev ◽  
Yu. G. Matvienko ◽  
A. V. Pankov ◽  
A. G. Kalinin
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
High Speed ◽  
Early Stage ◽  
Video Data ◽  
High Speed Video ◽  
Damage Diagnostics ◽  
Subsurface Defect ◽  
Sensitive Coating ◽  
Early Damage

The results of using early damage diagnostics technique (developed in the Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) for detecting the latent damage of an aviation panel made of composite material upon bench tensile tests are presented. We have assessed the capabilities of the developed technique and software regarding damage detection at the early stage of panel loading in conditions of elastic strain of the material using brittle strain-sensitive coating and simultaneous crack detection in the coating with a high-speed video camera “Video-print” and acoustic emission system “A-Line 32D.” When revealing a subsurface defect (a notch of the middle stringer) of the aviation panel, the general concept of damage detection at the early stage of loading in conditions of elastic behavior of the material was also tested in the course of the experiment, as well as the software specially developed for cluster analysis and classification of detected location pulses along with the equipment and software for simultaneous recording of video data flows and arrays of acoustic emission (AE) data. Synchronous recording of video images and AE pulses ensured precise control of the cracking process in the brittle strain-sensitive coating (tensocoating)at all stages of the experiment, whereas the use of structural-phenomenological approach kept track of the main trends in damage accumulation at different structural levels and identify the sources of their origin when classifying recorded AE data arrays. The combined use of oxide tensocoatings and high-speed video recording synchronized with the AE control system, provide the possibility of definite determination of the subsurface defect, reveal the maximum principal strains in the area of crack formation, quantify them and identify the main sources of AE signals upon monitoring the state of the aviation panel under loading P = 90 kN, which is about 12% of the critical load.

Data visualization quality in a geographic information system using golden ratio properties

2018 ◽  
pp. 46-57
Author(s):  
A. V. Voronin ◽  
G. N. Maltsev ◽  
M. Yu. Sokhen
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Data Visualization ◽  
Golden Ratio ◽  
Quality Criterion ◽  
Geographic Information ◽  
Control Device ◽  
Video Data ◽  
Geoinformation Systems ◽  
System Operator

Introduction:Data visualization quality is important for the work of a geographic information system operator, determining the conditions under which he or she makes decisions concerning the displayed data. Visual perception patterns associated with the golden ratio properties allow us to formulate a criterion for data visualization quality which would characterize the possibilities of the operator’s complex perception of the video data displayed on a control device screen in the form of an electronic card.Purpose:Substantiation of a data visualization quality criterion for geoinformation systems using the golden ratio properties, and the study of the conditions for providing good visualization quality for geodata and metadata on a video control device screen in accordance with the proposed criterion.Methods:A formal definition of the data visualization quality criterion in geoinformation systems using the coefficient of the screen area information coverage as an index whose optimal value corresponds to the mathematical definition of the golden ratio; and the study of the properties of this criterion. Results: Based on the conducted analysis of visual perception of video data and golden ratio properties during the data visualization, a criterion is proposed for data visualization quality, which uses the golden ratio properties and characterizes the possibilities of complex perception of video data in an electronic map form by a geographic information system operator. Iteration algorithms for choosing the video data display scale are developed, based on the visualization quality criterion and related to the golden ratio properties. These are the basic algorithm used for each geodata layer represented on the electronicmap, and an algorithm of successive analysis of various layers of the displayed geodata. The choice of a video data display scale in accordance with the developed algorithms can be preliminarily carried out by the system operator using the parameters of standard electronic maps and geodata/metadata sets typical for the current applied problem. We have studied how the scale of the geodata and metadata displayed on an electronic map affects their visualization quality on screens of various sizes. For the considered standard volumes of displayed geodata and metadata, the best visualization quality was achieved when they were displayed on a standard computer monitor, as opposed to a portable notebook or visualization screen.Practical relevance:The proposed criterion and the recommendations for choosing a screen size for the video monitoring device or the structures of the displayed geo-objects and metadata can be used in the design of geoinformation systems, or for preliminary choice of the displayed data structure by a geoinformation system operator.

Sign in / Sign up

Export Citation Format

Share Document