Video Shot Research Articles

Comparing conventional and action video game training in visual perceptual learning.

Action video game (AVG) playing has been found to transfer to a variety of laboratory tasks in visual cognition. More recently, it has even been found to transfer to low-level visual "psychophysics tasks. This is unexpected since such low-level tasks have traditionally been found to be largely "immune" to transfer from another task, or even from the same task but a different stimulus attribute, e.g., motion direction. In this study, we set out to directly quantify transfer efficiency from AVG training to motion discrimination. Participants (n = 65) trained for 20h on either a first-person active shooting video game, or a motion direction discrimination task with random dots. They were tested before, midway, and after training with the same motion task and an orientation discrimination task that had been shown to receive transfer from AVG training, but not from motion training. A subsequent control group (n = 18) was recruited to rule out any test-retest effect, by taking the same tests with the same time intervals, but without training. We found that improvement in motion discrimination performance was comparable between the AVG training and control groups, and less than the motion discrimination training group. We could not replicate the AVG transfer to orientation discrimination, but this was likely due to the fact that our participants were practically at chance for this task at all test points. Our study found no evidence, in either accuracy or reaction time, that AVG training transferred to motion discrimination. Overall, our results suggest that AVG training transferred little to lower-level visual skills, refining understanding of the mechanisms by which AVGs may affect vision. Protocol registration The accepted stage 1 protocol for this study can be found on the Open Science Framework at https://osf.io/zdv9c/?view_only=5b3b0c161dad448d9d1d8b14ce91ab11 . The stage 1 protocol for this Registered Report was accepted in principle on 01/12/22. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/ZDV9C.

Optimized deep learning enabled lecture audio video summarization

Video summarization plays an important role in multiple applications by compressing lengthy video content into compressed representation. The purpose is to present a fine-tuned deep model for lecture audio video summarization. Initially, the input lecture audio-visual video is taken from the dataset. Then, the video shot segmentation (slide segmentation) is done using the YCbCr space colour model. From each video shot, the audio and video within the video shot are segmented using the Honey Badger-based Bald Eagle Algorithm (HBBEA). The HBBEA is obtained by combining the Bald Eagle Search (BES) and Honey Badger Algorithm (HBA). The DRN training is executed by HBBEA to select the finest DRN weights. The relevant video frames are merged with the audio. The proposed HBBEA-based DRN outperformed with a better F1-Score of 91.9 %, Negative predictive value (NPV) of 89.6 %, Positive predictive value (PPV) of 90.7 %, Accuracy of 91.8 %, precision of 91 %, and recall of 92.8 %.

Video shot boundary detection based on frames objects comparison and scale-invariant feature transform technique

The most popular source of data on the Internet is video which has a lot of information. Automating the administration, indexing, and retrieval of movies is the goal of video structure analysis, which uses content-based video indexing and retrieval. Video analysis requires the ability to recognize shot changes since video shot boundary recognition is a preliminary stage in the indexing, browsing, and retrieval of video material. A method for shot boundary detection (SBD) is suggested in this situation. This work proposes a shot boundary detection system with three stages. In the first stage, multiple images are read in temporal sequence and transformed into grayscale images. Based on correlation value comparison, the number of redundant frames in the same shots is decreased, from this point on, the amount of time and computational complexity is reduced. Then, in the second stage, a candidate transition is identified by comparing the objects of successive frames and analyzing the differences between the objects using the standard deviation metric. In the last stage, the cut transition is decided upon by matching key points using a scale-invariant feature transform (SIFT). The proposed system achieved an accuracy of 0.97 according to the F-score while minimizing time consumption.

Robust Clustering-Based Automated Video Shot Boundary Detection Using Handcrafted and Deep Feature Fusion

Video shot boundary detection (VSBD) plays a key role in analyzing, summarizing, indexing and retrieving content-based data from videos. Many artificial intelligence (AI)-based techniques have recently been introduced to detect the gradual transition from video frames. However, those techniques fail to detect the presence of inter-classes like fade-in, fade-out and dissolve from gradual transition video frames. In addition, the existing techniques face high computational complexity during detecting transitions from the video frames. This research brings novel clustering-based techniques to classify the inter-classes like fade-in, fade-out and dissolve from gradual transition video frames. At the initial stage, color histogram differences (CHD) technique is introduced to detect the abrupt transition from the video frames. The identified abrupt transitions are completely removed from the video frames. Then, segmentation is done to segment the gradual transitions from the video frames. The segmented gradual transition frames are then given to extract the handcrafted and deep features from the video frames. The deep features are extracted from the segments using the Morlet Wavelet-assisted modified stacked autoencoder (MW-MSAE) technique. The extracted handcrafted features and deep features are then concatenated together, and finally, fused feature vectors are obtained. The fused features are then fed to the robust deep [Formula: see text]-means map clustering method (RDKMM) to aggregate based on similar features. For calculating the similar features, a similarity-based correlation calculation (SBCC) is done in adjacent frames to determine gradual shot transitions like fade-in, fade-out and dissolve. The dataset used in this research is the TREC Video Retrieval Evaluation (TRECVID) 2021 dataset. In the experimental scenario, an accuracy of 95.5%, a sensitivity of 95.3%, a specificity of 96.9%, a precision of 93.9%, an F-measure of 94.6% and Mathew’s correlation coefficient (MCC) of 92.4% are obtained.

When failure is not an option: a police firearms training concept for improving decision-making in shoot/don't shoot scenarios.

Sometimes, policing requires a quick and correct assessment of potentially hazardous situations. The training of tactical gaze control and visual attention, and its positive impact on efficient shoot/don't shoot decisions in police cadets' use of firearms has recently been demonstrated. On this basis, we designed an individual videobased police firearms training that was grounded on the Four-Component Instructional Design Model (4C/ID). We shifted toward an individual blended learning approach where we applied an intervention training focused on situational awareness, tactical gaze control, and visual attention. In a preregistered lab experiment, N = 45 senior police officers were randomly allocated to the intervention training or an active control training that resembled a traditional police firearms training. Both groups watched a self-produced educational video before proceeding to the practical training in our indoor firing range. In a pre- and post-test, they engaged in realistic shoot/don't shoot video scenarios. Both groups did very well regarding decision-making, the optimal muzzle position, and the tactical conduct to keep both eyes open before shooting. Although both groups performed on a comparable level in the pre-test's shoot scenarios, the intervention group significantly improved their response times and time until the first hit. Overall, we were able to provide an adapted, didactically based police firearms training that supplements current standards. We demonstrated that experts are still susceptible to innovative training concepts and therefore substantiate the recommendation to devote more attention to approaches that emphasize the importance of situational awareness, tactical gaze control, and visual attention in police firearms training.

A deep audio-visual model for efficient dynamic video summarization

The adage “a picture is worth a thousand words” resonates in the digital video domain, suggesting that a video could be seen as a composition of millions of these words. Videos are composed of countless frames. Video summarization creates cohesive visual units in scenes by condensing shots from segments. Video summarization gains prominence by condensing lengthy videos while retaining crucial content. Despite effective techniques using keyframes or keyshots in video summarization, integrating audio components is imperative. This paper focuses on integrating deep learning techniques to generate dynamic summaries enriched with audio. To address that gap, an efficient model employs audio-visual features, enriching summarization for more robust and informative video summaries. The model selects keyshots based on their significance scores, safeguarding essential content. Assigning these scores to specific video shots is a pivotal yet demanding task for video summarization. The model’s evaluation occurs on benchmark datasets, TVSum and SumMe. Experimental outcomes reveal its efficacy, showcasing considerable performance enhancements. On the TVSum, SumMe datasets, an F-Score metric of 79.33% and 66.78%, respectively, is achieved, surpassing previous state-of-the-art techniques.

A Video Shot Occlusion Detection Algorithm Based on the Abnormal Fluctuation of Depth Information

A Video Shot Occlusion Detection Algorithm Based on the Abnormal Fluctuation of Depth Information

Suspicious Activity Tracking

Abstract: Our offering includes a deep neural network model that can identify firearms in photographs and a machine learning and computer vision pipeline that can detect abandoned luggage in order to identify potential gun-based crime and circumstances involving abandoned luggage in surveillance film. Unusual behavior the technique of identifying undesired human activity in locations and circumstances is called detection. To do this, footage is converted into frames, and the processed frames are then used to analyze the people's sports. YOLOv3 is used to find a niche, in dubious sports like lock breaking and bag snatching, among others. Our gadget has a superb processing pace in addition to appropriate accuracy of detection. It is harder for computers to detect things in videos compared to images because of issues like blurriness or things getting blocked. They propose a solution called Shot Video Object Detector, which is a faster kind of detector for videos. It works by combining information from nearby frames to make better guesses about where objects are. Unlike other methods, Shot Video Object Detector does this by figuring out how things move between frames and then using that info to combine features. It also creates new features by borrowing information directly from neighboring frames using a special structure. Automated surveillance in public areas plays a crucial role in upholding law and order and proactively identifying potential risks to the public. Not only does the procedure automatically identify and detect known crooks, but it also tracks people's and things' movements and uses machine learning algorithms to alert the authorities to any questionable activity

Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning.

Adaptive video streaming relies on the construction of efficient bitrate ladders to deliver the best possible visual quality to viewers under bandwidth constraints. The traditional method of content dependent bitrate ladder selection requires a video shot to be pre-encoded with multiple encoding parameters to find the optimal operating points given by the convex hull of the resulting rate-quality curves. However, this pre-encoding step is equivalent to an exhaustive search process over the space of possible encoding parameters, which causes significant overhead in terms of both computation and time expenditure. To reduce this overhead, we propose a deep learning based method of content aware convex hull prediction. We employ a recurrent convolutional network (RCN) to implicitly analyze the spatiotemporal complexity of video shots in order to predict their convex hulls. A two-step transfer learning scheme is adopted to train our proposed RCN-Hull model, which ensures sufficient content diversity to analyze scene complexity, while also making it possible to capture the scene statistics of pristine source videos. Our experimental results reveal that our proposed model yields better approximations of the optimal convex hulls, and offers competitive time savings as compared to existing approaches. On average, the pre-encoding time was reduced by 53.8% by our method, while the average Bjøntegaard delta bitrate (BD-rate) of the predicted convex hulls against ground truth was 0.26%, and the mean absolute deviation of the BD-rate distribution was 0.57%.

Text-to-movie authoring of anatomy lessons

There is a need for a simple yet comprehensive tool to produce and edit pedagogical anatomy video courses, given the widespread usage of multimedia and 3D content in anatomy instruction. Anatomy teachers have minimal control over the present anatomical content generation pipeline. In this research, we provide an authoring tool for instructors that takes text written in the Anatomy Storyboard Language (ASL), a novel domain-specific language (DSL) and produces an animated video. ASL is a formal language that allows users to describe video shots as individual sentences while referencing anatomic structures from a large-scale ontology linked to 3D models. We describe an authoring tool that translates anatomy lessons written in ASL to finite state machines, which are then used to automatically generate 3D animation with the Unity 3D game engine. The proposed text-to-movie authoring tool was evaluated by four anatomy professors to create short lessons on the knee. Preliminary results demonstrate the ease of use and effectiveness of the tool for quickly drafting narrated video lessons in realistic medical anatomy teaching scenarios.

Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

INTRODUCTION: Sports video is a very important information resource. The classification of sports video with high accuracy can effectively improve the browsing and query effect of users. This project intends to study a motion video classification algorithm based on deep learning particle filter to solve the problems of strong subjectivity and low accuracy of existing motion video classification algorithms. A key box extraction method based on similarity is proposed. The moving video classification algorithm based on deep learning coding model is studied. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can greatly improve the classification performance of motion video.

A conceptual framework for panning as a screen recording technique in software education: Implications for instructional design in educational videos

Videos as instructional materials have become an important part of higher education. The ability of videos to be shared and used in social and educational settings demonstrates their increasingly significant contemporary impact. Identifying the video features that affect student engagement as a success indicator can therefore contribute to the effective use and design of educational videos. Panning is such a potential video recording and presentation feature/technique. The aim of this study is to reveal the ways panning is accomplished in videos and discuss implications for instructional design. Panning is to record a smaller section of a screen, relevant to the instructional activity, instead of recording the entire screen during video recording. It differs from regular video shots as it refers to the recording of the computer screen or any other display in that manner, whereas regular videos record everyday objects. It is a strategy used during the recording of educational videos developed for software education. It is a design consideration especially for creating educational materials to be displayed in mobile devices due to their small screens. To date, panning has not been comprehensively conceptualized as an educational technique for software education. In this study, the possible types of screen-recording in terms of panning are identified based on video review. Secondly, examples from one of the popular video-sharing sites — YouTube — are identified and presented using these panning types. Finally, a comparison is made between full-screen and panning videos. The implications of panning for instructional purposes are examined. A concise and useful guide is provided for instructional designers interested in creating more effective instructional videos.

Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Hybridized Neural Network Based Approaches Used for Video Shot Boundary Detection

Feature fusion over hyperbolic graph convolution networks for video summarisation

AbstractA novel video summarisation method called the Hyperbolic Graph Convolutional Network (HVSN) is proposed, which addresses the challenges of summarising edited videos and capturing the semantic consistency of video shots at different time points. Unlike existing methods that use linear video sequences as input, HVSN leverages Hyperbolic Graph Convolutional Networks (HGCNs) and an adaptive graph convolutional adjacency matrix network to learn and aggregate features from video shots. Moreover, a feature fusion mechanism based on the attention mechanism is employed to facilitate cross‐module feature fusion. To evaluate the performance of the proposed method, experiments are conducted on two benchmark datasets, TVSum and SumMe. The results demonstrate that HVSN achieves state‐of‐the‐art performance, with F1‐scores of 62.04% and 50.26% on TVSum and SumMe, respectively. The use of HGCNs enables the model to better capture the complex spatial structures of video shots, and thus contributes to the improved performance of video summarisation.

Video Scene Detection Using Transformer Encoding Linker Network (TELNet).

This paper introduces a transformer encoding linker network (TELNet) for automatically identifying scene boundaries in videos without prior knowledge of their structure. Videos consist of sequences of semantically related shots or chapters, and recognizing scene boundaries is crucial for various video processing tasks, including video summarization. TELNet utilizes a rolling window to scan through video shots, encoding their features extracted from a fine-tuned 3D CNN model (transformer encoder). By establishing links between video shots based on these encoded features (linker), TELNet efficiently identifies scene boundaries where consecutive shots lack links. TELNet was trained on multiple video scene detection datasets and demonstrated results comparable to other state-of-the-art models in standard settings. Notably, in cross-dataset evaluations, TELNet demonstrated significantly improved results (F-score). Furthermore, TELNet's computational complexity grows linearly with the number of shots, making it highly efficient in processing long videos.

Report of a magpie preying on a post-fledgling Daurian redstart.

A magpie (Pica pica) preying on a fledgling of Daurian redstart (Phoenicurus auroreus) was incidentally recorded with a video shot by mobile phone on 26 May 2021, providing direct evidence for magpie predation. It also shows that predation is an important factor that affects the survival of fledglings, indicating that survival of fledglings should be considered in evaluating breeding success of birds. The fledgling was about 13-day-old posthatching, and it was on its first day of leaving the nest when the incident occurred. It was preyed upon by a magpie 10 m away from the nest by two attempts under strong defensive behaviour from the female.

Human tracking robotic camera based on image processing for live streaming of conferences and seminars

There are numerous scenarios where the photographer is in difficulty and unable to capture or shoot video as required. This could be due to several factors such as limited space, decreased visibility, and an obstacle in the way. Therefore, this project implements the idea to capture and shoot video of the desired subject through an automatically controlled robotic camera with no need for a photographic bloke. The system comprises functions such as detection, tracking, live streaming, and video/audio recording along with the features of Radio-Frequency-Identification (RFID). Therefore, this robotic camera will detect the desired subject, track and focus it with the help of its position driven through movable motors sensing the RFID tag in case the object is non-stationary. The video/audio will be recorded on a computer along with the live streaming available on an Android-based device. The Viola-Jones algorithm of the image processing technique is used to detect the particular subject features and C for accessing the movable camera protocols. The RFID transmitter and receiver are used to sense the RFID card and serve the purpose to track the subject using the algorithms of image processing, with the advantage of ignoring other obstacles between the camera and the detected subject. Thus, adding a novel functionality to the existing systems, that lacks the feature of focusing the camera on the subject, when an obstacle is detected in between. The live streaming is achieved wirelessly through an open-source platform X-operating system, Apache, MySQL, Php, Perl (XAMPP). The idea is verified through concluded arrangements in self-made scenarios in response to the speed, distance, light, and background noise of the detected subject, which delivered encouraging results. Therefore, the designed system can be used for live conferences, seminars, and other multimedia-required arrangements.

Sentiment Analysis on Online Videos by Time-Sync Comments.

Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently.

Recurrent unit augmented memory network for video summarisation

AbstractVideo summarisation can relieve the pressure on video storage, transmission, archiving, and retrieval caused by the explosive growth of online videos in recent years. Most existing supervised video summarisation methods use convolutional neural network (CNN) or recurrent neural network (RNN) to model the temporal dependencies between video frames or video shots. CNN mainly focuses on local information, and RNN loses long‐term information when the input sequence is long, both of which have limited ability to obtain long‐range memory in the video. Therefore, a recurrent unit augmented memory network (RUAMN) for video summarisation is proposed, which effectively utilises the long‐term memory extraction ability of the end‐to‐end memory network (MemN2N) and solves the problem that MemN2N is insensitive to temporal sequence information. At the same time, the proposed RUAMN enhances the process of memory update between multiple computational steps (hops), and finally generates a meaningful video summarisation result. Specifically, the proposed RUAMN is mainly composed of the input module, the global‐and‐local sampling, the memory module and the output module. The input module uses a bidirectional GRU to obtain the forward and backward information of each video frame. Then the global‐and‐local sampling performs global sampling and local sampling on the output sequence of the input module respectively to obtain several shorter sequences, so that the memory modules can capture the fine‐grained relationship features between video frames more effectively. The memory module extracts the long‐term memory information in the feature sequence, and finally the frame‐level importance scores are predicted by the output module. Extensive experiments on benchmark datasets, that is, TVSum and SumMe, demonstrate the superiority of our method over several state‐of‐the‐art supervised video summarisation methods.

SIGNAL TRANSMISSION OPTIMIZATION MOVING-VECTOR CLASSIFICATION MPEG STANDARD VIDEO CODING

The rise of useful multimedia applications has sparked a rush to perfect video compression methods. Over the last decade, there has been a lot of interest in using motion-compensated frame interpolation (MCFI) methods to boost video frame rate during playback in both the academic and consumer electronics sectors. Interpolation refers to the process of defining the values of a function at places that are not represented by any of the examples. It accomplishes this process by fitting a constant function across the discrete input samples. We based our methodology on the fact that color cameras function similarly to the human eye. We have been used to black and white photographs, yet the real world is full of color. Grayscale images were the sole option until the advent of color monitors and cameras. Energy assessment calls for photo capture using a camera. Light's electromagnetic waves provide the observable energy in this scheme. For motion-compensated video vector frame interpolation or frame rate up-conversion and also Phase frame interpolation Method, the authors present a novel approach with low vector dispensation method in the conclusion. Using optical flow or other conventional methods, it is necessary to have precise pixel correspondences between pictures in order to compute interpolated frames in a video series. We provide a practical replacement by capitalizing on advances in phase-based approaches, which use the phase shift of individual pixels to express motion. In challenging interpolation conditions, such as major appearance changes, flow-based algorithms frequently create severe visual distortions, yet our solution fails gently. Our approach is particularly useful for retiming and interpolating frames in high-resolution video shot at a high frame rate. Estimating motion for the purpose of improving the quality of the decompressed video requires more research on the nature of the compression problem. a novel way to estimating motion that is analogous to full-search 3ss,4ss, block-matching, and parametric techniques. The primary focus will be research into both error-free compression and the development of novel motion estimation algorithms. The results of the research indicate that the suggested system has the potential to increase visual superiority and is also robust, especially when applied to video sequences with quick movements and challenging portions. By combining phase-based and conventional Lagrangian, which is utilized to get better quality results and deal with variations in light more gracefully.