Videos Of Animals Research Articles

Societal Perception of Animal Videos on Social Media-Funny Content or Animal Suffering? A Survey.

On social media, numerous animal videos are uploaded and viewed every day. However, these videos, which are apparently funny for humans, are often associated with animal suffering. In this study, 3246 participants of an online survey were asked about their personal perception of animal videos on social media, about recognising animal suffering in these videos, and about their respective reactions. A total of 98.5% of participants who used social media already saw animal videos. Participants most frequently viewed informative videos (52.9%), followed by funny/entertaining animal videos (41.8%). For 45.8% of participants, animal suffering was often recognisable in animal videos. Female participants were more likely to recognise animal suffering than male participants (p < 0.001), and participants living in a rural residence were more likely to recognise it than those from an urban residence (p = 0.017). Furthermore, 62.5% of participants had left a critical comment or disliked a video with animal suffering. Animal videos seem to be highly popular on social media, but animal suffering may go unnoticed in funny videos. The fact that 91.8% of participants want a warning label for animal suffering in videos shows that social media users would like to see animal welfare be given more prominence on social media.

Meowmy, Pawrents and Menschenwelpen ‘human puppies’: Linguistic practices of Doing Interspecies Families on German Instagram

Looking at cute animal images and videos has become a popular online pastime and animals themselves increasingly appear as actors in social media who have their own accounts and profiles from which they ‘speak’ to and ‘interact’ with us. This development goes hand in hand with a change in human-animal relations in most of Western cultures, where pets – especially cats and dogs – are more and more often seen and treated as family members while the animals we eat are rendered invisible in factory farms that are spatially detached from everyday human life. The familial relationships between humans and their pet companions are constructed by a variety of sociocultural means, one of which is language. By talking about pets as babies and children and oneself as cat or dog moms, the connection between the human and the pet is discursively established as one of kinship and intimacy rather than one of ownership and obedience. Social media have become a prolific space for performing “interspecies families” (Owens, 2015), where self-acclaimed pet parents can interact with each other and build communities of practice in which pet accounts post and engage with each other from an imagined pet perspective. This article uses a quantitative corpus-based approach to study the discursive construction of interspecies families on German-speaking Instagram. By analysing more than 20,000 posts from dog and cat accounts offering first person narratives, the article provides insights into the ways human pet owners speak about themselves and their relations with their pets from an imagined pet perspective. The analysis shows that the caption texts accompanying the posted pictures and videos are highly anthropocentric: at the centre of these short texts stands the – usually female – human pet owner. Using family terminology for both humans and other animals in these posts serves the linguistic construction of interspecies families in which pets and their human companions are held together by love and kinship ties rather than ownership.

Application of a novel deep learning-based 3D videography workflow to bat flight.

Studying the detailed biomechanics of flying animals requires accurate three-dimensional coordinates for key anatomical landmarks. Traditionally, this relies on manually digitizing animal videos, a labor-intensive task that scales poorly with increasing framerates and numbers of cameras. Here, we present a workflow that combines deep learning-powered automatic digitization with filtering and correction of mislabeled points using quality metrics from deep learning and 3D reconstruction. We tested our workflow using a particularly challenging scenario: bat flight. First, we documented four bats flying steadily in a 2 m3 wind tunnel test section. Wing kinematic parameters resulting from manually digitizing bats with markers applied to anatomical landmarks were not significantly different from those resulting from applying our workflow to the same bats without markers for five out of six parameters. Second, we compared coordinates from manual digitization against those yielded via our workflow for bats flying freely in a 344 m3 enclosure. Average distance between coordinates from our workflow and those from manual digitization was less than a millimeter larger than the average human-to-human coordinate distance. The improved efficiency of our workflow has the potential to increase the scalability of studies on animal flight biomechanics.

An automated approach for human-animal conflict minimisation in Assam and protection of wildlife around the Kaziranga National Park using YOLO and SENet Attention Framework

Human-animal conflict in Assam, India's north-eastern state, is rising continuously. Because it occurs year-round, it damages agricultural productivity and kills people and animals, including elephants. When a herd of wild elephants emerges from a deep forest and enters human-inhabited territory around the Kaziranga National Park (KNP) in Assam, an alert must be sounded for the neighbourhood residents and forest workers to prevent conflicts. Another concern is that many wild animals die near the KNP while crossing the national highway NH-37 which traverses the area. During floods, animals flee to the highlands for food and shelter. An automated animal identification and warning system near the KNP may reduce human-animal confrontations. This paper reports the design of a system that attempts to address the above concerns. Artificial Intelligence (AI)-based strategies are utilized to recognize wild animals from live video sequences, provide warnings to avoid encounters, and protect humans and animals. Deep learning models and YoloV5 with the SENet attention layer are used to recognize wild animals in real-time. This model is trained using a public and customized dataset of animal species. Cameras attached to the cloud-based AI system take photographs from several KNP locations to confirm the model. The model's 96% accuracy in animal photographs and videos taken day and night and in feed from contemporaneous location has shown its utility. The model also improves reliability by 1–13% over previous methods.

ContrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals

In recent years, supervised machine learning models trained on videos of animals with pose estimation data and behavior labels have been used for automated behavior classification. Applications include, for example, automated detection of neurological diseases in animal models. However, we identify two potential problems of such supervised learning approach. First, such models require a large amount of labeled data but the labeling of behaviors frame by frame is a laborious manual process that is not easily scalable. Second, such methods rely on handcrafted features obtained from pose estimation data that are usually designed empirically. In this paper, we propose to overcome these two problems using contrastive learning for self-supervised feature engineering on pose estimation data. Our approach allows the use of unlabeled videos to learn feature representations and reduce the need for handcrafting of higher-level features from pose positions. We show that this approach to feature representation can achieve better classification performance compared to handcrafted features alone, and that the performance improvement is due to contrastive learning on unlabeled data rather than the neural network architecture. The method has the potential to reduce the bottleneck of scarce labeled videos for training and improve performance of supervised behavioral classification models for the study of interaction behaviors in animals.

The effect of different types of TikTok dance challenge videos on young women's body satisfaction

The aim of this paper was to investigate the effect of different types of TikTok dance challenge videos on young women's body satisfaction. Study 1 involved 262 women aged between 18 and 25 years, who were randomly assigned to watch TikTok videos featuring either thin dancers, large dancers, or amusing animal videos. Body satisfaction significantly increased after watching large dancer TikTok videos and decreased after watching thin dancer TikTok videos, although this decrease was not significant. Study 2 replicated and extended study 1 and involved 280 women aged between 18 and 25 years. Females body satisfaction significantly decreased after watching the thin dancer TikTok videos and significantly increased after watching the large dancer TikTok videos. There was no significant difference between the participants in terms of state appearance comparison. Females in the thin dancer condition engaged in upward social comparison, whereas the females in the large dancer condition engaged in downward social compression. The impact of video type was not moderated either by trait appearance comparison, or thin ideal internalization. The effect of video type was not mediated by state appearance comparison but was fully mediated by the direction of social comparison. Study 3 involved 375 women aged between 18 and 25 years and found that body satisfaction was lower after watching the thin dancers than after watching the large dancers. This study shows that findings from the previous two studies could not be explained by the demand characteristics in the design of these studies. All three studies show that exposure to large dancers on TikTok has a positive impact on women's body satisfaction, whereas exposure to thin dancers has a negative impact on women's body satisfaction. The implications of these findings for the design of social media are discussed.

Examining the Effects of Nature and Animal Videos on Stress

ABSTRACT Animals are often prohibited in public areas, which limit opportunities for people to experience improvements in well-being from their presence. Visual representations of animals, such as videos, may serve as an alternative intervention to the physical presence of live animals. University students, in particular, may benefit from virtual animal-assisted interventions, given that they report high rates of stress, and opportunities to interact with live animals at tertiary institutions are generally limited. This study recruited 200 participants from introductory psychology courses through a research participant pool at Toronto Metropolitan University in Canada. Following exposure to a mental arithmetic stressor that was 3 minutes in length, participants were randomly assigned to watch a 3-minute video that featured either a dog, puppy, nature, or blank screen. In light of previous research that found that infantile facial features elicit greater attention and positive affect among viewers compared with mature facial features, this study hypothesized that the video of the puppy would be the most effective at enhancing wellbeing. Data were collected through the Depression Anxiety Stress Scales (DASS-21), Social Phobia Inventory (SPIN), Positive and Negative Affect Schedule (PANAS), Visual Analog Scale (VAS), and the Subjective Appraisal of Video Affects (SAVA). The results revealed that the puppy video produced significantly higher scores of happiness relative to the nature and control videos (F (8.54,557.94) = 3.29, p < 0.001, ηp 2 = 0.05). Additionally, participants reported that the puppy video was significantly more effective at inducing relaxation compared with the control video (F (3,199) = 7.58, p < 0.001, ηp 2 = 0.10). These results suggest that watching a video of a puppy can be useful to improve wellbeing among university students. This study offers novel contributions to the limited literature examining the impact of visual representations of infant animals on stress.

Morphological characteristics influence the spatial mixing patterns of shorebirds at Shengjin Lake.

The coexistence of species with similar ecological niches is one of the core interests of community ecology research. However, how functional feeding traits, including bill size and leg length, determine the niche of mixed flocks of shorebird species has seldomly been studied, as well as, microhabitat variables affect the spatial patterns of availability and the quality of patches for wintering. From October 2016 to March 2017 at Shengjin Lake, Anhui Province, China, we recorded 226 scan samples from the different microhabitats and 93 focal animal videos of four common shorebird species: common greenshank, spotted redshank, Kentish plover, and little ringed plover. We found that the species participating in the mixed groups were different in each microhabitat. The results of the overlap index for microhabitats and foraging techniques between the species were consistent with the morphological characteristics of these species. Kentish and little ringed plovers had the highest Pianka's niche overlap index values of 0.95 and 0.98 for microhabitats and foraging techniques, respectively, whereas common greenshank and spotted redshank had values of 0.78 and 0.89, respectively. Common greenshank and spotted redshank used four foraging techniques: a single probe (PR), multiple probes (MPR), a single peck (PE), and multiple pecks (MPE). Kentish and little ringed plovers only used PE and MPE. The mean bill size, mean leg length, and mean foraging frequency were significantly associated with water depth. The mean bill size and mean leg length were both significantly correlated with the mean foraging frequency of shorebirds. The vegetated area was the most important variable for grouping among shorebirds. We concluded that the four species showed differences in their preferred microhabitats and foraging patterns. Interspecific morphological differences, including bill and leg lengths, resulted in niche differentiation. Thus, effective resource allocation by regional species was realized, and a dynamic balance was achieved by the mixed foraging species. The information on foraging behavior and habitat requirements could be useful in the management of water levels in natural areas and conservation of a diversity of wintering shorebirds.

Examining the Impact of Virtual Animal Stimuli on College Students' Affect and Perception of their Academic Advising Experience.

The benefits of animal-assisted interventions (AAI) involving animals in therapy are widely accepted. The presence of animals in therapy can decrease a patient's reservation about therapy and promote a sense of comfort and rapport during the therapy process. Using survey data from college students (n = 152) attending a large public four-year institution, this study is the first to investigate the benefits of virtual animal stimuli during academic advising appointments. It posits that exposure to virtual animal stimuli can influence positive mental health and well-being in academic advising settings. Specifically, the research questions explored how different types of video content influence students' affect and how virtual animal stimuli impact students' perception of their advisor and university. College students were randomly assigned to watch one of four types of virtual stimuli (wild animals, companion animals, nature, and a control) prior to their advising session. Subjective measures were collected at baseline and after the advising session. Results indicated animal stimuli increase positive affect, and companion animal stimuli influence the student's perception of the advisor. This study supports the notion that companion animal videos positively impact students' well-being and interactions with their advisors and may have broader implications beyond the academic setting.

Interspecific association of sika deer in terrestrial animal communities of Liancheng National Nature Reserve, China.

The prevention and control of invasive of alien species is an important work for nature reserves. This study analyzes the development trend of the alien species sika deer in Liancheng National Nature Reserve. From October 2019 to June 2020, 3523 valid photos and videos of terrestrial animals were acquired from 130 camera traps, and sika deer were recorded in 21 photos from 13 traps. The survival of the sika deer population was investigated by means of morphological identification, population structure analysis, species relative abundance indices, and species spatial association analysis. A total of 13 sika deer individuals were identified by camera trapping, including two kids and three subadults representing the reproductive capacity of the population. Spatially, sika deer is not associated with any local species and was outside the spatial association network of terrestrial animals in Liancheng National Nature Reserve, indicating that the sika deer population has not been integrated into the local community and has failed to perform its ecological function. It is worth noting that the reserve provides habitat suitable for sika deer and that the population has adequate reproductive capacity. Due to the lack of large apex predators in the reserve, the population size of ungulates such as sika deer, red deer, and Siberian roe deer may expand and lead to population outbreaks and the associated problems for the ecosystem. To restore large- and medium-sized carnivores and avoid the population outbreak of the species, the present challenges require immediate attention in Liancheng National Nature Reserve.

Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a 2D navigation task. We show how the use of animal videos improves performance over RL algorithms that do not leverage such observations.

Sharks and Rays 101: A Virtual Program Connecting Families with Estuary Research

Virtual programming presents new opportunities to connect learners with aquarium ambassador animals, communicate science, and foster appreciation of coastal habitats. Educators can share additional resources, such as video footage and researcher presentations, that would otherwise be unavailable to aquarium guests viewing exhibits. Starting in the summer of 2020, the University of Georgia Marine Education Center and Aquarium (MECA) hosted new virtual public programs, including a Sharks and Rays 101 webinar. This webinar was offered twice; in summer 2020 it had 46 attendees, including 17 children and 29 adults, and in spring 2021 it had 25 attendees, including 6 children and 19 adults. The webinar used a place-based approach, introducing local elasmobranch species and shark nursery research conducted in Georgia estuaries. Estuaries are important nursery habitats for many shark species and areas where individuals recreationally fishing may interact with elasmobranchs. Activities included a virtual visit featuring a stingray and skate, participant polls, Q &amp; A with a researcher, and interpreting maps from shark surveys. The program was evaluated with a voluntary survey and pre- and post-tests. Surveys indicated that future webinars, including those for adults, could increase engagement by incorporating videos of ambassador animals, polls, and limiting slide presentation length. An accompanying classroom activity was developed to extend learning after the webinar, utilizing a recording of the live event. This paper describes the webinar, shares the classroom activity, and suggests modifications that may be used by other educators to virtually teach about shark nurseries in estuaries.

Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems

Digitally extracted biometrics from visible videos of farm animals could be used to automatically assess animal welfare, contributing to the future of automated veterinary support systems. This study proposed using non-invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) located at the Dookie campus, The University of Melbourne, Australia. Data extracted from dairy cows were used to develop two machine learning models: a biometrics regression model (Model 1) targeting (i) somatic cell count, (ii) weight, (iii) rumination, and (iv) feed intake and a classification model (Model 2) mapping features from dairy cow's face to predict animal age. Results showed that Model 1 achieved a high correlation coefficient (R = 0.96), slope (b = 0.96), and performance, and Model 2 had high accuracy (98%), low error (2%), and high performance without signs of under or overfitting. Models developed in this study can be used in parallel with other models to assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms. • A visible RGB camera was used to assess dairy cows biometrics. • Machine learning (ML) classification model developed to predict cows age. • ML regression model developed to predict cows weight, somatic cells, feed intake and rumination.

Adults with Animal Phobia: Systematic Replication of Clinical Cases

AbstractAnimal phobia is common in children, but it is also frequent in adults who maintain the problem throughout their lives. Eleven cases of animal phobia that were treated with progressive multimedia exposure are presented. The participants were two men and nine women (aged 19–27 years), with anxiety and avoidance problems in relation to various animals. A single-case A-B-Follow-up design was carried out, with concurrent control of several baselines; and a systematic between-subjects replication, with the same treatment repeated across different participants and types of phobias. A behavioural interview, phobia questionnaires, and Multimedia Behavioural Avoidance Test (M-BAT) with pictures and videos of animals, as well as heart rate, were used for assessment purposes. The intervention was the progressive multimedia exposure in four phases (photographs, videos, simulated animals, and live exposure), in addition to diaphragmatic breathing, and homework assignments. The results replicate the same efficacy in each of the participants, with statistically significant and clinical changes in their daily lives. Also, the data as a group show this success with a high Cohen's d effect size (between −1.63 and −30.03). We conclude with an appraisal of the usefulness of the procedure for adults with phobias who do not tolerate direct exposure.

Artificial Intelligence and Social Networks Can Help to Protect the Famous Dugong

Large land and ocean mammals, like elephants and whales, play essential roles in the environment but are severely threatened by human activities. Images taken from planes can be used to spot these animals from the air. However, these species are difficult to observe because they are rare and move a lot, so unfortunately there are not many images collected from planes that contain these species. Fortunately, however, people share many photos and videos of large animals on social networks because these animals are attractive. These images have been used by researchers to train a computer program to recognize an endangered ocean mammal: the dugong. The program found up to 79% of the dugongs present in images collected by a plane flying around the main island of New Caledonia, which is in the Pacific Ocean. The goal is to use this program to automatically count and map dugongs in New Caledonia.

Animal eMotion, or the emotional evaluation of moving animals

ABSTRACT Responding adequately to the behaviour of human and non-human animals in our environment has been crucial for our survival. This is also reflected in our exceptional capacity to detect and interpret biological motion signals. However, even though our emotions have specifically emerged as automatic adaptive responses to such vital stimuli, few studies investigated the influence of biological motion on emotional evaluations. Here, we test how the motion of animals affects emotional judgements by contrasting static animal images and videos. We investigated this question (1) in non-fearful observers across many different animals, and (2) in observers afraid of particular animals across four types of animals, including the feared ones. In line with previous studies, we find an idiosyncratic pattern of evoked emotions across different types of animals. These emotions can be explained to different extents by regression models based on relevant predictor variables (e.g. familiarity, dangerousness). Additionally, our findings show a boosting effect of motion on emotional evaluations across all animals, with an additional increase in (negative) emotions for moving feared animals (except snakes). We discuss implications of our results for experimental and clinical research and applications, highlighting the importance of experiments with dynamic and ecologically valid stimuli.

Increasing Vocabulary Mastery Through Animals Video At Kindergarten PGRI Bulucenrana

The objective of the research was the find out whether or not of animal videos increases vocabulary mastery in PGRI Bulucenrana kindergarten students. This study uses an experimental design. The population of this research is one group, group B in TK PGRI Bulucenrana. In the academic year 2018/2019 with a total population of 12 students. The sample of this study was 12 students. The sample of this study was 12 students or a total sample. This study used media animals video in class. Researchers found that the use of animal videos can increase vocabulary mastery in Bulucenrana PGRI kindergarten students. The average value of the initial test (21,08) is lower than the average value of the final test (77,50), it is also evidenced by the P-Value (0,000 &lt; 0,05) a significant level of 0,05. So the researchers concluded that H0 was rejected and H1 was accepted. Based on data analysis, the researcher concludes that: animals video media is effective in increasing vocabulary mastery in Bulucenrana PGRI kindergarten students&#x0D;

Facial expression in humans as a measure of empathy towards farm animals in pain.

People often express concern for the welfare of farm animals, but research on this topic has relied upon self-report. Facial expressions provide a quantifiable measure of emotional response that may be less susceptible to social desirability bias and other issues associated with self-report. Viewing other humans in pain elicits facial expressions indicative of empathy. Here we provide the first evidence that this measure can also be used to assess human empathetic responses towards farm animals, showing that facial expressions respond reliably when participants view videos of farm animals undergoing painful procedures. Participants (n = 30) were asked to watch publicly sourced video clips of cows and pigs undergoing common management procedures (e.g. disbudding, castration, tail docking) and control videos (e.g. being lightly restrained, standing). Participants provided their subjective rating of the intensity of 5 negative emotions (pain, sadness, anger, fear, disgust) on an 11-point Likert scale. Videos of the participants (watching the animals) were scored for intensity of unpleasantness of the participants’ facial expression (also on an 11-point Likert scale) by a trained observer who was blind to treatment. Participants showed more intense facial expressions while viewing painful procedures versus control procedures (mean ± SE Likert; 2.4 ± 0.08 versus 0.6 ± 0.17). Participants who reported more intense negative responses also showed stronger facial expressions (slope ± SE = 0.4 ± 0.04). Both the self-reported and facial measures varied with species and procedure witnessed. These results indicate that facial expressions can be used to assess human-animal empathy.

Exploring How White-Faced Sakis Control Digital Visual Enrichment Systems.

Simple SummaryMany zoo-housed primates use visual computer systems for enrichment but little is known about how monkeys would choose to control these devices. Here we investigate what visual enrichment white-faced saki monkeys would trigger and what effect these videos have on their behaviour. To study this, we built an interactive screen device that would trigger visual stimuli and track the sakis’ interactions when using the system. Over several weeks, we found that the sakis would trigger underwater and worm videos significantly more than animal, abstract art and forest videos, and the control condition of no-stimuli. Further, the sakis triggered the animal video significantly less often over all other conditions. Yet, viewing their interactions over time, the sakis’ usage of the device followed a bell curve, suggesting novelty and habituation factors. As such, it is unknown if the stumli or devices novelty and habituation caused the changes in the sakis interactions over time. These results also indicated that the different visual stimuli conditions significantly reduced the sakis’ scratching behaviour with the visual stimuli conditions compared to the control condition. Further, the usage of visual stimuli did not increase the sakis’ looking at and sitting in front of the screen behaviours. These results highlight problems in defining interactivity and screen usage with monkeys and screens from looking behaviours and proximity alone.Computer-enabled screen systems containing visual elements have long been employed with captive primates for assessing preference, reactions and for husbandry reasons. These screen systems typically play visual enrichment to primates without them choosing to trigger the system and without their consent. Yet, what videos primates, especially monkeys, would prefer to watch of their own volition and how to design computers and methods that allow choice is an open question. In this study, we designed and tested, over several weeks, an enrichment system that facilitates white-faced saki monkeys to trigger different visual stimuli in their regular zoo habitat while automatically logging and recording their interaction. By analysing this data, we show that the sakis triggered underwater and worm videos over the forest, abstract art, and animal videos, and a control condition of no-stimuli. We also note that the sakis used the device significantly less when playing animal videos compared to other conditions. Yet, plotting the data over time revealed an engagement bell curve suggesting confounding factors of novelty and habituation. As such, it is unknown if the stimuli or device usage curve caused the changes in the sakis interactions over time. Looking at the sakis’ behaviours and working with zoo personnel, we noted that the stimuli conditions resulted in significantly decreasing the sakis’ scratching behaviour. For the research community, this study builds on methods that allow animals to control computers in a zoo environment highlighting problems in quantifying animal interactions with computer devices.

Skeletonizing the Dynamics of Soft Continuum Body from Video.

Soft continuum bodies have demonstrated their effectiveness in generating flexible and adaptive functionalities by capitalizing on the rich deformability of soft material. Compared with a rigid-body robot, it is in general difficult to model and emulate the morphology dynamics of a soft continuum body. In addition, a soft continuum body potentially has an infinite degree of freedom, requiring considerable labor to manually annotate its dynamics from external sensory data such as video. In this study, we propose a novel noninvasive framework for automatically extracting the skeletal dynamics from video of a soft continuum body and show the applications and effectiveness of our framework. First, we demonstrate that our framework can extract skeletal dynamics from animal videos, which can be effectively utilized for the analysis of soft continuum body including animal motion. Next, we focus on a soft continuum arm, a commonly used platform in soft robotics, and evaluate the potential information-processing capability. Normally, to control such a high-dimensional system, it is necessary to introduce many sensors to completely capture the motion dynamics, causing the deterioration of the material's softness. We illustrate that the evaluation of the memory capacity and sensory reconstruction error enables us to verify the minimum number of sensors sufficient for fully grasping the state dynamics, which is highly useful in designing a sensor arrangement for a soft robot. Also, we release the software developed in this study as open source for biology and soft robotics communities, which contributes to automating the annotation process required for the motion analysis of soft continuum bodies.