scholarly journals Behavior Trajectory Tracking of Piglets Based on DLC-KPCA

Agriculture ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 843
Author(s):  
Chengqi Liu ◽  
Han Zhou ◽  
Jing Cao ◽  
Xuchao Guo ◽  
Jie Su ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Principal Component ◽  
Automatic Monitoring ◽  
Semantic Space ◽  
Feature Model ◽  
Kernel Principal Component Analysis ◽  
Tracking Accuracy ◽  
Five Dimensions ◽  
Space Angle ◽  
Tracking Model

Tracking the behavior trajectories in pigs in group is becoming increasingly important for welfare feeding. A novel method was proposed in this study to accurately track individual trajectories of pigs in group and analyze their behavior characteristics. First, a multi-pig trajectory tracking model was established based on DeepLabCut (DLC) to realize the daily trajectory tracking of piglets. Second, a high-dimensional spatiotemporal feature model was established based on kernel principal component analysis (KPCA) to achieve nonlinear trajectory optimal clustering. At the same time, the abnormal trajectory correction model was established from five dimensions (semantic, space, angle, time, and velocity) to avoid trajectory loss and drift. Finally, the thermal map of the track distribution was established to analyze the four activity areas of the piggery (resting, drinking, excretion, and feeding areas). Experimental results show that the trajectory tracking accuracy of our method reaches 96.88%, the tracking speed is 350 fps, and the loss value is 0.002. Thus, the method based on DLC–KPCA can meet the requirements of identification of piggery area and tracking of piglets’ behavior. This study is helpful for automatic monitoring of animal behavior and provides data support for breeding.

scholarly journals Deep Learning-based Automatic Monitoring of Pigs Physico-temporal Activities at Different Greenhouse Gas Concentrations

2021 ◽  
Author(s):  
Anil Bhujel ◽  
Elanchezhian Arulmozhi ◽  
Byeong Eun Moon ◽  
Hyeon Tae Kim
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Automatic Monitoring ◽  
Physical Activities ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Short Term ◽  
Health And Wellbeing ◽  
High Detection ◽  
Tracking Model

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs' health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs' health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect short-term pigs' physical activities in a compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00-08:00, 13:00-14:00, and 20:00-21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Fast-er R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results showed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs also shortened their sternal-lying posture increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in monitoring and tracking pigs' physical activities non-invasively.

scholarly journals Deep-Learning-Based Automatic Monitoring of Pigs’ Physico-Temporal Activities at Different Greenhouse Gas Concentrations

Animals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 3089
Author(s):  
Anil Bhujel ◽  
Elanchezhian Arulmozhi ◽  
Byeong-Eun Moon ◽  
Hyeon-Tae Kim
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Automatic Monitoring ◽  
Physical Activities ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Short Term ◽  
Health And Wellbeing ◽  
High Detection ◽  
Tracking Model

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs’ health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs’ health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs’ short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00–08:00, 13:00–14:00, and 20:00–21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in the monitoring and tracking of pigs’ physical activities non-invasively.

scholarly journals On the Move: Principal Components of the Functions and Experiences of Mobile Music Listening

2021 ◽  
Vol 4 ◽  
pp. 205920432110328
Author(s):  
Mia Kuch ◽  
Clemens Wöllner
Keyword(s):  
Life Experiences ◽  
Principal Component ◽  
Underlying Structure ◽  
Music Listening ◽  
Social Dimensions ◽  
Online Questionnaire ◽  
Mood Management ◽  
Five Dimensions ◽  
Mobile Music ◽  
Deep Listening

Mobile music listening is widely recognized as an integral part of everyday music use. It is also a rather peculiar experience, since the listeners are surrounded by strangers in public and at the same time engaged in a solitary and private activity. The current study aimed at investigating the functions and experiences of mobile listening with a quantitative online questionnaire, and collected further information about mobile listening situations and listening habits. Among respondents ( n = 203), 89% reported listening to music while being on the move. We found mood-related and cognitive functions to be most prevalent (e.g., enhancing mood, relaxation, prevention of being bored), whereas least important functions relate to social dimensions (e.g., feeling less lonely, feeling less watched). Regarding experiences of mobile music, respondents most commonly adapted their mood to the music and lost touch with the current surroundings. A principal component analysis on ratings of functions and experiences resulted in an underlying structure of five dimensions, representing different levels of involvement: (1) Mood Management comprises functions to satisfy individual needs; (2) Absorption and Aestheticization encompasses deep listening experiences and altered perception of the surroundings; (3) Social Encapsulation and Self-Focus describe the distancing of oneself and changes in attention; (4) Distraction and Passing Time include the prevention of being bored and making time pass faster; and (5) Auditory Background is defined by a non-attentive and rather unaffected music listening. These results highlight the immersiveness of mobile music listening. By creating an individual soundworld, listeners distance themselves from the surroundings aurally and mentally, and modify their attention, perception, moods, and emotions, leading to an improvement of daily life experiences while moving.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

Data Mining in Analysis of Biomechanical Signals

2009 ◽  
Vol 147-149 ◽  
pp. 588-593 ◽  
Author(s):  
Marcin Derlatka ◽  
Jolanta Pauk
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Cerebral Palsy ◽  
Spina Bifida ◽  
Decision Tree ◽  
Principal Component ◽  
Data Preprocessing ◽  
Component Analysis ◽  
Kernel Principal Component Analysis

In the paper the procedure of processing biomechanical data has been proposed. It consists of selecting proper noiseless data, preprocessing data by means of model’s identification and Kernel Principal Component Analysis and next classification using decision tree. The obtained results of classification into groups (normal and two selected pathology of gait: Spina Bifida and Cerebral Palsy) were very good.

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