First-person Video Research Articles

Rights Quest: A Game for Child Rights Awareness

The interactive video game "A Way Home!" is a serious game for promoting pro-social change by raising awareness of children's rights, as outlined in the United Nations Convention on the Rights of the Child treaty. All countries except the United States have ratified this treaty. Through a collaborative effort, faculty and students developed this game using an open-source platform to create an immersive first-person video game experience to disseminate crucial knowledge about children's rights and the need for ratification. Game design guides players through real-world comparative scenarios depicting environments that support or do not support those fundamental rights. Through engaging gameplay and storytelling, iterative development processes facilitated the game's effectiveness in conveying complex concepts, such as the distinction between rights and responsibilities. The significance of children's fundamental rights, such as access to food, a safe, clean home, the right to speech, and freedom of expression, as enshrined in the CRC, are emphasized by juxtaposing different environments and depicting the consequences of choices. Grounded upon the Diffusion of Innovations theory (Rogers 2010), the first step to adopting cultural change is education. Using gameplay elements, including rewards for positive choices and learner guidance instructions, "A Way Home!" rights quest motivates players to explore and understand the principles of children's rights. The player wins the game when they achieve the goal of being heard in a court of law. By combining education with entertainment, the game aims to inspire empathy and foster understanding, with the ultimate goal of teaching about the need for ratification of the CRC Treaty in the U.S. and empowering players to take action and advocate for children's rights. This paper depicts the methodology involving participants (n=17) from one southeastern university in the States to capture qualitative and quantitative feedback through a perception survey to evaluate the first demo model. Results support advancing data collection for the next version developed using virtual reality (V.R.) headsets to further immerse the learners in the experience.

An egocentric video and eye-tracking dataset for visual search in convenience stores

We introduce an egocentric video and eye-tracking dataset, comprised of 108 first-person videos of 36 shoppers searching for three different products (orange juice, KitKat chocolate bars, and canned tuna) in a convenience store, along with the frame-centered eye fixation locations for each video frame. The dataset also includes demographic information about each participant in the form of an 11-question survey. The paper describes two applications using the dataset — an analysis of eye fixations during search in the store, and a training of a clustered saliency model for predicting saliency of viewers engaged in product search in the store. The fixation analysis shows that fixation duration statistics are very similar to those found in image and video viewing, suggesting that similar visual processing is employed during search in 3D environments and during viewing of imagery on computer screens. A clustering technique was applied to the questionnaire data, which resulted in two clusters being detected. Based on these clusters, personalized saliency prediction models were trained on the store fixation data, which provided improved performance in prediction saliency on the store video data compared to state-of-the art universal saliency prediction methods.

Unsupervised mapping and semantic user localisation from first-person monocular video

We propose an unsupervised probabilistic framework for learning a human-centred representation of a person’s environment from first-person video. Specifically, non-geometric maps modelled as hierarchies of probabilistic place graphs and view graphs are learned. Place graphs model a user’s patterns of transition between physical locations whereas view graphs capture an aspect of user behaviour within those locations. Furthermore, we describe an implementation in which the notion of place is divided into stations and the routes that interconnect them. Stations typically correspond to rooms or areas where a user spends time. Visits to stations are temporally segmented based on qualitative visual motion. We describe how to learn maps online in an unsupervised manner, and how to localise the user within these maps. We report experiments on two datasets, including comparison of performance with and without view graphs, and demonstrate better online mapping than when using offline clustering.

Hazard Perception in Visually Impaired Drivers Who Use Bioptic Telescopes.

Bioptic telescopic spectacles can allow individuals with central vision impairment to obtain or maintain driving privileges. The purpose of this study was to (1) compare hazard perception ability among bioptic drivers and traditionally licensed controls, (2) assess the impact of bioptic telescopic spectacles on hazard perception in drivers with vision impairment, and (3) analyze the relationships among vision and hazard detection in bioptic drivers. Visual acuity, contrast sensitivity, and visual field were measured for each participant. All drivers completed the Driving Habits Questionnaire. Hazard perception testing was conducted using commercially available first-person video driving clips. Subjects signaled when they could first identify a traffic hazard requiring a change of speed or direction. Bioptic drivers were tested with and without their bioptic telescopes in alternating blocks. Hazard detection times for each clip were converted to z-scores, converted back to seconds using the average response time across all videos, and then compared among conditions. Twenty-one bioptic drivers and 21 normally sighted controls participated in the study. The hazard response time of bioptic drivers was improved when able to use the telescope (5.4 ± 1.4 seconds vs 6.3 ± 1.8 seconds without telescope); however, it remained significantly longer than for controls (4.0 ± 1.4 seconds). Poorer visual acuity, contrast sensitivity, and superior visual field sensitivity loss were related to longer hazard response times. Drivers with central vision loss had improved hazard response times with the use of bioptic telescopic spectacles, although their responses were still slower than normally sighted control drivers. The use of a bioptic telescope by licensed, visually impaired drivers improves their hazard detection speed on a video-based task, lending support to their use on the road.

Digital storytelling online: a case report exploring virtual design, implementation opportunities and challenges

BackgroundDigital storytelling is an arts-informed approach that engages short, first-person videos, typically three to five minutes in length, to communicate a personal narrative. Prior to the pandemic, digital storytelling initiatives in health services research were often conducted during face-to-face workshops scheduled over multiple days. However, throughout the COVID-19 lockdowns where social distancing requirements needed to be maintained, many digital storytelling projects were adapted to online platforms.MethodsAs part of a research project aiming to explore the day surgery treatment and recovery experiences of women with breast cancer in Peel region, we decided to pivot our digital storytelling process to an online format. During the process, we observed that the online digital storytelling format had multiple opportunities and challenges to implementation.ResultsThis paper outlines our promising practices and lessons learned when designing and implementing an online digital storytelling project including pre-production, production and post-production considerations.ConclusionsWe provide lessons learned for future teams intending to conduct an online digital storytelling project.

Utilizing Advanced Technologies for eLearning in Clerkship

The clerkship phase in medical school is pivotal for clinical training. In response to the COVID-19 pandemic, the adoption and implementation of educational technology in medical education became inevitable, leading most medical schools to shift toward distance learning. While technologies cannot entirely replace hands-on clinical teaching, faculty members have devised effective ways to utilize technology, such as virtual patients, virtual reality, first-person video streaming, simulators, gamification, and mobile apps. This article provides a summary of the technologies employed, the challenges associated with them, and the advantages and instructional outcomes resulting from their use.

Raising patient voices in medical education: an assessment of patient perceived effect of social determinants of health conversations and the patient-physician relationship on quality of obstetric care, to inform the development of patient driven medical education curricula.

Conventional medical education lacks the lived experiences of patients which may authentically convey the social determinants of health (SDOH) and resulting health disparities. Videos of first-person patient narratives may prove a valuable education tool in this regard. The objective of this study is to investigate how patient demographics, satisfaction with care, and patient-physician relationships influence obstetric patient interest and willingness to contribute to a SDOH video curriculum by sharing their lived experiences through first-person narratives. Study design included an anonymous, cross-sectional survey and an optional semi-structured telephone interview. Participants were 18 years old with a live-birth delivery <8 weeks prior to recruitment and received care during their pregnancy at Los Angeles General Medical Center (LAGMC). Variables surveyed included demographics, satisfaction with care, aspects of the patient-physician relationship, perceived utility, and personal interest in contributing to an educational SDOH video. A bivariate analysis was conducted to compare participants' characteristics and responses on interest in contributing and perceived helpfulness of first-person patient SDOH videos. 72.43% of participants (N = 70) believed a patient's first-person video on SDOH would be "Helpful" in preparing physicians to provide competent medical care; however, 71.43% responded "No" to "Interest" in sharing with physicians their experiences with SDOH. English preference and being U.S. born were factors significantly associated with viewing first-person SDOH video as "Helpful" (P > 0.001). Major themes from telephone interviews reflected enthusiasm for first-person patient narratives and perceived benefits of using patient experiences to educate physicians on SDOH. However, participants cited barriers to disclosing SDOH including brief and strictly clinical interactions with physicians, lack of continuity of care, and fear of being judged by physicians. While most participants recognized the utility of addressing social needs in medical education and reported satisfaction with their obstetricians and care, these factors did not uniformly translate into willingness to contribute first-person patient narratives. To improve the representation of patients from racial, ethnic, gender, linguistic, and sexual minorities into medical curricula, further research and strategies are needed to overcome the barriers discouraging patient disclosure of social needs to physicians.

Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry

The well-known methods of scene extraction on video are focused on analyzing the similarity between frames. However, they do not all analyze the composition of the image scene, which may remain the same during maintenance. Therefore, this paper proposes an algorithm for equipment maintenance scene detection based on human hand tracking. It is based on the assumption that, when servicing technological equipment, it is possible to determine the change in repair action by the position of the service engineer’s hands. Thus, certain information and the algorithm that processes these changes allow us to segment the video into actions performed during the service. We process the time series obtained by moving the hand position using spectral singular value decomposition for multivariate time series. To verify the algorithm, we performed maintenance on the control cabinet of a mining conveyor and recorded the work on a first-person video, which was processed using the developed method. As a result, we obtained some scenes corresponding to opening the control cabinet, de-energizing the unit, and checking the contacts with a multimeter buzzer test. A third-person video of motor service was similarly processed. The algorithm demonstrated the results in separate scenes of removing screws, working with a multimeter, and disconnecting and replacing motor parts.

First-Person Video Domain Adaptation With Multi-Scene Cross-Site Datasets and Attention-Based Methods

Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person video action recognition is an under-explored problem, with a lack of benchmark datasets and limited consideration of first-person video characteristics. Existing benchmark datasets provide videos with a single activity scene, e.g. kitchen, and similar global video statistics. However, multiple activity scenes and different global video statistics are still essential for developing robust UDA networks for real-world applications. To this end, we first introduce two first-person video domain adaptation datasets: ADL-7 and GTEA_KITCHEN-6. To the best of our knowledge, they are the first to provide multi-scene and cross-site settings for UDA problem on first-person video action recognition, promoting diversity. They provide five more domains based on the original three from existing datasets, enriching data for this area. They are also compatible with existing datasets, ensuring scalability. First-person videos have unique challenges, i.e. actions tend to occur in hand-object interaction areas. Therefore, networks paying more attention to such areas can benefit common feature learning in UDA. Attention mechanisms can endow networks with the ability to allocate resources adaptively for the important parts of the inputs and fade out the rest. Hence, we introduce channel-temporal attention modules to capture the channel-wise and temporal-wise relationships and model their inter-dependencies important to this characteristic. Moreover, we propose a Channel-Temporal Attention Network (CTAN) to integrate these modules into existing architectures. CTAN outperforms baselines on the new datasets and one existing dataset, EPIC-8.

ViWise: Fusing Visual and Wireless Sensing Data for Trajectory Relationship Recognition

People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. However, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%.

A Combination of Caffeine, TeaCrine, and Dynamine Improves the Neurophysiological and Performance Measures of Electronic (E)-Gamers.

Video games require precise motor skills, quick reaction times, and cognitive engagement. The tremendous growth of the electronic (e)-gaming industry has increased the demands for cognitive supplements (e.g., nootropics) to help e-athletes gain a competitive edge. The primary aim of this study was to evaluate the effects of combined caffeine + TeaCrine + Dynamine measures of neurophysiological and first-person shooter game performance in e-gamers. Using a randomized double-blinded, crossover design, we assessed the effects of an acute, single-dose treatment of caffeine (200 mg) vs. caffeine (200 mg) + TeaCrine (10 mg) + Dynamine (50 mg) (CTD) vs. Ppacebo (maltodextrin). Each participant was tested under all three conditions one week apart. Baseline and post-dose measures were tested one hour apart. Participants [n = 49 male (24.4 ±, 4.5 yr)] were amateur e-gamers who play a first-person video game for at least 10 hours/week. Gaming performance was assessed through a series of first-person shooter training games through AIMLAB (State Space Labs, Inc., New York,USA). These included Reflex Shot (RS) standard, speed, and precision. The neurophysiological activity was captured while participants played three games through a single-channel EEG. In the standard game, the caffeine and the CTD conditions shot significantly more targets relative to the placebo, and both caffeine and the CTD condition had significantly greater targets post-dose compared to pre-dose. However, both the placebo and caffeine conditions had significantly slower reaction times post-dose compared to pre-dose. In the speed game, both the caffeine and placebo conditions shot a significantly greater number of targets, while the placebo and caffeine conditions had significantly more shots post-dose compared to pre-dose. Only the CTD condition had a significant increase in total kills post-dose compared to pre-dose. In the precision game, only the CTD condition significantly improved the number of kills per second post-dose, while only the caffeine condition had more shots post-dose. EEG data collected concomitantly with game playing showed that the CTD condition resulted in significantly lower alpha power compared to the placebo condition. The CTD group also showed increased theta activity post-dose during game playing compared to both the placebo caffeine conditions. CTD appears to improve overall shooting gaming performance and neurophysiological measures of cognitive activity compared to caffeine and placebo. Collectively, these findings suggest that CTD assists with speed-accuracy tradeoffs where caffeine-only can lead to erratic play; thus, CTD may be particularly beneficial for shooting precision. The EEG data support this notion since the CTD exhibited lower alpha power suggesting increased cognitive flexibility and arousal and higher theta power suggesting greater cognitive control and decision-making under pressure.

Assessing the association between overcrowding and human physiological stress response in different urban contexts: a case study in Salzburg, Austria

Overcrowding in densely populated urban areas is increasingly becoming an issue for mental health disorders. Yet, only few studies have examined the association between overcrowding in cities and physiological stress responses. Thus, this study employed wearable sensors (a wearable camera, an Empatica E4 wristband and a smartphone-based GPS) to assess the association between overcrowding and human physiological stress response in four types of urban contexts (green space, transit space, commercial space, and blue space). A case study with 26 participants was conducted in Salzburg, Austria. We used Mask R-CNN to detect elements related to overcrowding such as human crowds, sitting facilities, vehicles and bikes from first-person video data collected by wearable cameras, and calculated a change score (CS) to assess human physiological stress response based on galvanic skin response (GSR) and skin temperature from the physiological data collected by the wristband, then this study used statistical and spatial analysis to assess the association between the change score and the above elements. The results demonstrate the feasibility of using sensor-based measurement and quantitative analysis to investigate the relationship between human stress and overcrowding in relation to different urban elements. The findings of this study indicate the importance of considering human crowds, sitting facilities, vehicles and bikes to assess the impact of overcrowding on human stress at street level.

Generating Personalized Summaries of Day Long Egocentric Videos.

The popularity of egocentric cameras and their always-on nature has lead to the abundance of day long first-person videos. The highly redundant nature of these videos and extreme camera-shakes make them difficult to watch from beginning to end. These videos require efficient summarization tools for consumption. However, traditional summarization techniques developed for static surveillance videos or highly curated sports videos and movies are either not suitable or simply do not scale for such hours long videos in the wild. On the other hand, specialized summarization techniques developed for egocentric videos limit their focus to important objects and people. This paper presents a novel unsupervised reinforcement learning framework to summarize egocentric videos both in terms of length and the content. The proposed framework facilitates incorporating various prior preferences such as faces, places, or scene diversity and interactive user choice in terms of including or excluding the particular type of content. This approach can also be adapted to generate summaries of various lengths, making it possible to view even 1-minute summaries of one's entire day. When using the facial saliency-based reward, we show that our approach generates summaries focusing on social interactions, similar to the current state-of-the-art (SOTA). The quantitative comparisons on the benchmark Disney dataset show that our method achieves significant improvement in Relaxed F-Score (RFS) (29.60 compared to 19.21 from SOTA), BLEU score (0.68 compared to 0.67 from SOTA), Average Human Ranking (AHR), and unique events covered. Finally, we show that our technique can be applied to summarize traditional, short, hand-held videos as well, where we improve the SOTA F-score on benchmark SumMe and TVSum datasets from 41.4 to 46.40 and 57.6 to 58.3 respectively. We also provide a Pytorch implementation and a web demo at https://pravin74.github.io/Int-sum/index.html.

Future pedestrian location prediction in first-person videos for autonomous vehicles and social robots

Future pedestrian trajectory prediction in first-person videos offers great prospects to help autonomous vehicles and social robots to enable better human-vehicle interactions. Given an egocentric video stream, we aim to predict the location and depth (distance between the observed person and the camera) of his/her neighbors in future frames. To locate their future trajectories, we mainly consider three main factors: a) It is necessary to restore the spatial distribution of pedestrians in 2D image to 3D space, i.e., to extract the distance between the pedestrian and the camera which is often neglected. b) It is critical to utilize neighbors’ poses to recognize their intentions. c) It is important to learn human-vehicle interactions from the pedestrian’s historical trajectories. We propose to incorporate these three factors into a multi-channel tensor to represent the main features in real-life 3D space. We then put this tensor into an innovative end-to-end fully convolutional network based on transformer architecture. Experimental results reveal our method outperforms other state-of-the-art methods on public benchmarks MOT15, MOT16 and MOT17. The proposed method will be useful to understand human-vehicle interaction and helpful for pedestrian collision avoidance.

Does optic flow provide information about actions?

Optic flow, the pattern of light generated in the visual field by motion of objects and the observer's body, serves as information that underwrites perception of events, actions, and affordances. This visual pattern informs the observer about their own actions in relation to their surroundings, as well as those of others. This study explored the limits of action detection for others as well as the role of optic flow. First-person videos were created using camera recordings of the actor's perspective as they performed various movements (jumping jacks, jumping, squatting, sitting, etc.). In three experiments participants attempted to detect the action from first-person video footage using open ended responses (Experiment 1), forced-choice responses (Experiment 2), and a match-to-sample paradigm (Experiment 3). It was discovered that some actions are more difficult to detect than others. When the task was challenging (Experiment 1) athletes were more accurate, but this was not the case in Experiments 2 and 3. All actions were identified above chance level across viewpoints, suggesting that invariant information was detected and used to perform the task.

A Day in the Life: Characterization of Doctoral Bench Research in Synthetic Chemistry Using Phenomenological Case Studies.

Despite decades of reform efforts, STEM education continues to face calls for improvement, especially regarding the teaching laboratory. Establishing an empirical understanding of the types of hands-on, psychomotor skills that students need to learn to succeed in downstream careers could help ensure laboratory courses are promoting authentic learning. Therefore, this paper reports phenomenological grounded theory case studies characterizing the nature of benchwork in synthetic organic chemistry graduate research. Through first-person video data and retrospective interviews, the results illustrate how organic chemistry students use psychomotor skills to conduct doctoral research and where they acquired those skills. By understanding the role that psychomotor skills play in authentic benchwork and the role that teaching laboratories play in the development of those skills, chemical educators could revolutionize undergraduate laboratory experiences by enabling evidence-based incorporation of the psychomotor component into laboratory learning objectives.

First-person video experiences as a vicarious, virtual alternative to in-person basic science labs.

The global COVID-19 pandemic required the teaching of basic science instructional laboratories be done in a remote, online format termed emergency remote teaching (ERT). The aims of this study were to: (1) share strategies for ERT of basic science instructional laboratories and (2) assess student perceptions of the experience of virtual demonstrations that were recorded from the first-person perspective of the professor. Laboratories for courses in gross anatomy, neuroanatomy, and clinical microbiology were adapted to ERT by creating videos that allowed the students to view the laboratory activities through the eyes and hands of the faculty. A unique 5-question survey instrument was created to collect students' perceptions of gross anatomy, neuroanatomy, and clinical microbiology virtual lab experiences. Percentage of responses were calculated for 4 close-ended questions. Qualitative content analysis was conducted on the single open-ended question. Two additional close-ended questions were used for assessing perception of gross anatomy labs only. Videos of gross anatomy, neuroanatomy, and clinical microbiology laboratory activities mimicked the student experience through the camera lens as labs were performed by faculty members engaged in either dissection, viewing structures or doing experiments, respectively. In all 3 basic science courses, over 70% of students strongly agreed or agreed that the videos created a sense of being in the laboratory. Use of video technology allowed faculty to mimic the student experience of being in basic science laboratories, and, importantly, allowed the student to virtually participate in the learning experience.

First-person video recordings with eye tracking glasses and cognitive task analysis as a framework for referee decision training

ABSTRACT Rationale In comparison to players little is known about how sports officials integrate perception and cognition to manage in-game decisions. Design Using a naturalistic approach this paper uses first-person eye-tracked video footage to document the attentional demands and situation awareness (SA) of expert touch (rugby/football) referees in their real-world environment to inform decision training for amateur officials. Drawing directly from match performances, an applied cognitive task analysis (ACTA) technique investigated how three international referees manage complex attentional demands, to see what lessons could be learned for less-experienced referees. Findings Referees emphasised the importance of role clarity and game understanding as the foundation for effective match officiating. They used advanced cues such as player body language and movement patterns (SA1) to interpret game status (SA2) and predict likely actions and movement patterns (SA3). Ordering abstraction, preventive communication and early positioning were used to lessen cognitive load and encourage game flow. Practical Implications The merits of using first-person, eye-tracked, audio-visual footage with ACTA for training less experienced sports officials through expert verbal elicitation or self-reflection are discussed. Research Contribution The paper proposes a decision tree for touch refereeing which emphasises a hierarchical ordering of cognitive decision points that provides the basis for training amateur referees.

Perspectives and expectations of stroke survivors using egocentric cameras for monitoring hand function at home: a mixed methods study

Purpose Most stroke survivors have remaining upper limb impairment six months after stroke and require additional rehabilitation and help from family members to enhance their performance of daily activities. First-person (egocentric) video has been proposed to capture the activities of daily living (ADLs) of stroke survivors in order to assess their hand function at home. This study explored the experiences and expectations of stroke survivors regarding the use of egocentric cameras in daily life for rehabilitation applications. Methods Twenty-one chronic stroke survivors recruited for the study were asked to record three sessions of 1.5 h of video of their ADLs at home over two weeks. Their experiences and expectations after completing the recordings were discussed using a structured questionnaire and a semi-structured interview. The questionnaire and interview data were analysed using descriptive statistics and content analysis, respectively. The results were further integrated using a mixed methods analysis for mutual explanation and elaboration. Results The themes generated were Camera Usability, Privacy Concerns Related to Home Recordings, Future Use of the Camera in Public, and Information Usefulness. The participants perceived that the camera was easy to use, the information obtained from the recordings was beneficial, and no major concerns about recording at home. A discreet camera and a solution to privacy issues were prerequisites to recording tasks in public. Conclusions There was high acceptance among stroke survivors regarding the use of wearable cameras for rehabilitation purposes in the future. Concerns to be managed include discomfort, self-consciousness, and the privacy of others. Implications for rehabilitation The egocentric camera was easy for the stroke survivors to use at home. However, they expressed a preference for cameras to be less noticeable and lighter in the future to minimize self-consciousness and discomfort. Expectations for future use of an egocentric camera for upper limb rehabilitation at home from the perspectives of stroke survivors included receiving feedback on their hand function in daily life and guidance on how to improve function. Privacy concerns of stroke survivors regarding recording activities of daily living were mostly avoidable by planning in advance. However, some personal hygiene tasks and virtual meetings were recorded by accident. A checklist of common activities that may raise privacy issues can be provided along with the camera to serve as a reminder to avoid these issues.

The salience network is activated during self-recognition from both first-person and third-person perspectives.

We usually observe ourselves from two perspectives. One is the first-person perspective, which we perceive directly with our own eyes, and the other is the third-person perspective, which we observe ourselves in a mirror or a picture. However, whether the self-recognition associated with these two perspectives has a common or separate neural basis remains unclear. To address this, we used functional magnetic resonance imaging to examine brain activity while participants viewed pretaped video clips of themselves and others engaged in meal preparation taken from first-person and third-person perspectives. We found that the first-person behavioral videos of the participants and others induced greater activation in the premotor-intraparietal region. In contrast, the third-person behavioral videos induced greater activation in the default mode network compared with the first-person videos. Regardless of the perspective, the videos of the participants induced greater activation in the salience network than the videos of others. On the other hand, the videos of others induced greater activation in the precuneus and lingual gyrus than the videos of the participants. These results suggest that the salience network is commonly involved in self-recognition from both perspectives, even though the brain regions involved in action observation for the two perspectives are distinct.