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Inspirational and fun, this action-packed book explains the video-making process from script to screen, with techniques to try out and practical tips to produce exciting projects at home. Discover how to get the best angles, lighting, and sound quality, and add special effects when recording using phone, webcam, camera, or camcorder. Turn footage into a finished product by adding visual effects with editing software, and find out how to format, upload, and premiere the masterpiece. Whether recording special events, pets, sports, music videos, or a stop-motion animation, this book has everything you need! The book's content supports the STEAM (Science, Technology, Engineering, Art, Math) approach to cross-curricular learning.
Book
Why students do not turn on their video cameras during online classes and an equitable and inclusive plan to encourage them to do so
Enrollment in courses taught remotely in higher education has been on the rise, with a recent surge in response to a global pandemic. While adapting this form of teaching, instructors familiar with traditional face‐to‐face methods are now met with a new set of challenges, including students not turning on their cameras during synchronous class meetings held via videoconferencing. After transitioning to emergency remote instruction in response to the COVID‐19 pandemic, our introductory biology course shifted all in‐person laboratory sections into synchronous class meetings held via the Zoom videoconferencing program. Out of consideration for students, we established a policy that video camera use during class was optional, but encouraged. However, by the end of the semester, several of our instructors and students reported lower than desired camera use that diminished the educational experience. We surveyed students to better understand why they did not turn on their cameras. We confirmed several predicted reasons including the most frequently reported: being concerned about personal appearance. Other reasons included being concerned about other people and the physical location being seen in the background and having a weak internet connection, all of which our exploratory analyses suggest may disproportionately influence underrepresented minorities. Additionally, some students revealed to us that social norms also play a role in camera use. This information was used to develop strategies to encourage—without requiring—camera use while promoting equity and inclusion. Broadly, these strategies are to not require camera use, explicitly encourage usage while establishing norms, address potential distractions, engage students with active learning, and understand your students’ challenges through surveys. While the demographics and needs of students vary by course and institution, our recommendations will likely be directly helpful to many instructors and also serve as a model for gathering data to develop strategies more tailored for other student populations. Students were asked why they chose not to turn on their cameras during synchronous class meetings held via Zoom. Their responses influenced a strategy for encouraging them to do so.
Journal Article
Measuring Reactance to Camcorder Symbols Linked to Online News
The aim of the study is to test the validity of a short-scale measuring reactance to a camera symbol associated with online news, indicating the possibility of viewing video footage. The operationalization of reactance means a mixture of anger and negative cognitions preceded by the sense of danger of losing one’s freedom (Reynolds-Tylus, 2019). As the brief reactance scale elaborated by Hall and colleagues (2017) contains these elements, we assumed that it would constitute the appropriate basis for the elaboration of further brief reactance scales. Thus, we elaborated a brief reactance scale adequate for measuring reactance to camcorder symbols linked to online news. Data collection took place among the students of Sapientia University (Romania). For analysing the adequacy of the scale, we used confirmatory factor analysis, reliability analysis, and convergent validity analysis. We also checked to what extent the elaborated measuring tool could predict the avoidance of news linked with camcorder symbols. The results of this analysis show that in the case of people with high reactance to camcorder symbols, the increase of reactance leads to these people choosing less and less news linked with camcorder symbols. All these prove that despite its limitations the scale constitutes an adequate tool for the measuring of reactance to camcorder symbols.
Journal Article
Inter-session repeatability of markerless motion capture gait kinematics
The clinical uptake and influence of gait analysis has been hindered by inherent limitations of marker-based motion capture systems, which have long been the standard method for the collection of gait data including kinematics. Markerless motion capture offers an alternative method for the collection of gait kinematics that presents several practical benefits over marker-based systems. This work aimed to determine the reliability of lower limb gait kinematics from video based markerless motion capture using an established experimental protocol for testing reliability. Eight healthy adult participants performed three sessions of five over-ground walking trials in their own self-selected clothing, separated by an average of 8.5 days, while eight synchronized and calibrated cameras recorded video. Three-dimensional pose estimates from the video data were used to compute lower limb joint angles. Inter-session variability, inter-trial variability, and the variability ratio were used to assess the reliability of the gait kinematics. Compared to repeatability studies based on marker-based motion capture, inter-trial variability was slightly greater than previously reported for some angles, with an average across all joint angles of 2.5°. Inter-session variability was smaller on average than all previously reported values, with an average across all joint angles of 2.8°. Variability ratios were all smaller than those previously reported with an average of 1.1, indicating that the multi-session protocol increased the total variability of joint angles by 10% of the inter-trial variability. These results indicate that gait kinematics can be reliably measured using markerless motion capture.
Journal Article
Driver crash risk factors and prevalence evaluation using naturalistic driving data
The accurate evaluation of crash causal factors can provide fundamental information for effective transportation policy, vehicle design, and driver education. Naturalistic driving (ND) data collected with multiple onboard video cameras and sensors provide a unique opportunity to evaluate risk factors during the seconds leading up to a crash. This paper uses a National Academy of Sciences-sponsored ND dataset comprising 905 injurious and property damage crash events, the magnitude of which allows the first direct analysis (to our knowledge) of causal factors using crashes only. The results show that crash causation has shifted dramatically in recent years, with driver-related factors (i.e., error, impairment, fatigue, and distraction) present in almost 90% of crashes. The results also definitively show that distraction is detrimental to driver safety, with handheld electronic devices having high use rates and risk.
Journal Article
A Review of Vision-Based On-Board Obstacle Detection and Distance Estimation in Railways
This paper provides a review of the literature on vision-based on-board obstacle detection and distance estimation in railways. Environment perception is crucial for autonomous detection of obstacles in a vehicle’s surroundings. The use of on-board sensors for road vehicles for this purpose is well established, and advances in Artificial Intelligence and sensing technologies have motivated significant research and development in obstacle detection in the automotive field. However, research and development on obstacle detection in railways has been less extensive. To the best of our knowledge, this is the first comprehensive review of on-board obstacle detection methods for railway applications. This paper reviews currently used sensors, with particular focus on vision sensors due to their dominant use in the field. It then discusses and categorizes the methods based on vision sensors into methods based on traditional Computer Vision and methods based on Artificial Intelligence.
Journal Article