Test time inference: In order to improve the accuracy, many start-of-the-art algorithms prefer to run some post- processing. For instance, TSN  and ARTNet , each segment video collects 25 individual frames. Each frame/volume sample crops 10 areas of corner and center and their horizontal flipping. Then based on the average scores of the 250 samples results are obtained. It is clear that methods for calculation are very expensive and that is not the best way for fast videoanalysis algorithm. However, our algorithm without too much additional operations, is based on many widely recognized ideas to solve the problem of speed. During the test period, our algorithm in video is divided into N subsection and randomly extracts a frame from each section, these frames only perform center cropping and send the cropped part to the network. This is an end-to-end prediction.
Abstract— Video analytics, loosely defined as autonomous understanding of events occurring in a scene. The use of deceptive techniques in user-generated video portals is ubiquitous. Unscrupulous up loaders deliberately mislabel video descriptors aiming at increasing their views and subsequently their ad revenue. This problem, usually referred to as "click-bait," may severely undermine user experience. In this work, we study the click-bait problem on YouTube by collecting metadata for 206k videos. To address it, we devise a deep learning model based on variation auto-encoders that supports the diverse modalities of data that videos include. Every click earns them display advertisement revenue. Social media users who are tricked into clicking may experience a sense of disappointment or agitation, and social media operators have been observing growing amounts of click bait on their platforms. As largest video-sharing platform on the web, YouTube, too, suffers from click bait. Thus, it is susceptible to recommending misleading videos to users. Keywords— Videoanalysis, Clickbait, Sentiment analysis, Machine Learning, Transfer Learning
enables the full spectrum of video processing, enhancement, and analysis, and image clarification routines produce greater degrees of contrast, allowing easier identification of objects and text from the background. Both of these products run on commercially available hardware running the Microsoft Windows XP operating system and Adobe Premiere Professional. These products provide a complete forensic videoanalysis package to meet all of your investigative needs.
The current exploratory paper aims to make a contribution to this emerging sociological field by addressing the role of arts interventions and engagements for people living with dementia. This research makes a number of innovative contributions. First, it demonstrates in line with other studies (see for instance the papers presented at the ‘Atypical Interaction Conference’ 2016, Center for Social Practices and Cognition, University of Southern Denmark) the sociological relevance of applying videoanalysis to a dementia-related setting. Second, it aligns with Büscher (2005), Mittelman & Epstein (2009) as well as Basting et al. (2016) to shift the focus to people with dementia and their interactions by capturing their interactions in situ. Third, it seeks to present an alternative picture of meaningful conduct that does not focus solely on oral communication (see Beard 2011: 634), but also considers the interactions with objects (Latour 1996).
The Aerobic Gymnastics is a complex sport and the movements are performed continuously, intensely at high speed with the musical accompaniment (Code of Points, 2013-2016). Gymnastics may be globally defined as any physical exercise on the floor or apparatus that promotes endurance, strength, flexibility, agility, coordination, and body control (Peter Werner, Lori Williams, Tina Hall, 2012). One can directly assess the overall performance to the naked eye, but is not able to assess the individual elements of movement and technical aspects (Raiola, 2012). The videoanalysis indirectly, through the ability to stop and review the various stages of movement several times, facilitates the evaluation. Aerobic Gymnastic is the ability to perform complex movements produced by the traditional aerobic exercises, in a continuous manner, with high intensity, perfectly integrated with soundtracks. This sport is performed in a aerobic/anaerobic lactacid condition and expects the
With this phase of the examination completed, the removable footplate is added to the table at a suitable height to allow the mother to sit in a sideways posi- tion facing the radiologist. She will then hold the child on her lap, the patient also facing the radiologist. Administration of the contrast either by bottle or syringe will then be repeated, commencing the video run immediately before the contrast is administered. Apart from identifying any aberration of swallowing, the presence or absence of an adenoid pad in the post-nasal space should be noted. The presence of a large adenoid pad will significantly compli- cate any existing inco-ordination. The lat- eral study completed, the child will then rotate through 90° and face the tube. An anteroposterior study will then be repeated in the erect position, a manoeuvre that will identify the symmetrical use of both lateral food channels or alternatively one of the commonest variations, the unilateral food channel filling.
In literature,  were the first to borrow ideas from classification systems for automatic analysis of visual art and studied the di ff erences between paintings and photographs. Image features such as edges, spatial variation of colors, num- ber of unique colors, and pixel saturation were used for classification.  compared van Gogh with his contemporaries by statistical analysis of a mas- sive set of automatically extracted brushstrokes.  introduced the problem of artistic image annotation and retrieval and proposed several solutions us- ing graph-based learning techniques.  proposed a SOM-based model for studying and visualizing the relationships among painting collections of di ff er- ent painters.  presented an analysis of the a ff ective cues extracted from abstract paintings by looking at low-level features and employing a bag-of- visual-words approach. Few works focused specifically on inferring style from paintings [51, 86]. However, none of these works have studied the problem of decoupling artist-specific and style-specific patterns as we do with our multi- task dictionary learning framework.
If the ambient sound present on an audio recording changes abruptly, this could indicate that the environment where the recording took place suddenly changed. The volume and tone of a voice on the recording can provide clues as to distance and spatial relationships within a scene. Lighting conditions can be examined to estimate the time of day or environmental conditions at the time of the recording. Technical details may also confirm information about a recording. For instance, an unnatural waveform present in the audio or video signal may indicate that an edit has been made. A physical identifier may be present in the signal on magnetic tape that can identify it as a copy or indicate that it was recorded on a particular device. Sometimes, a perpetrator will try to destroy audio or video evidence; however, using these methods, the recording can be analyzed to determine what occurred. In the famous Watergate investigation, a great deal of effort was spent examining an 18½-‐minute gap in an audio recording of President Richard Nixon discussing the Watergate break in with his Chief of Staff. Analysis of the audio signature  left
In recent years, with the remarkable increase of video data generated and distributed through networks, there is an ev- ident need to develop an intelligent video browsing and in- dexing system. To build such a system and facilitate content- based video accessing, automatic semantic extraction is a prerequisite and a challenge to multimedia-understanding systems. Therefore, semantic videoanalysis and annotation have received much interest and attracted research e ﬀ orts. The previous research works [1–3] attempt to extract the se- mantics from visual and motion information. However, the investigation on extracting the semantic information from multimodal data is still very limited. In this paper, we de- velop tools based on visual, motion, and audio information for analyzing and annotating basketball video using both low-level features and domain knowledge. In particular, we show that the multimodal-based approach can generate re- liable annotation for basketball video which cannot be suc- cessfully achieved using a single mode. We address the prob- lem of semantic basketball videoanalysis and annotation for MPEG compressed videos using multimodal information. The problem has three related aspects: (1) analyze the struc- ture of the basketball video, (2) locate the potential posi- tions where an interesting event occurs, and (3) represent the results in an annotation file utilizing standardized descrip- tions. Since the semantic understanding of video content is
Cardiovascular problems are evolving as the chief cause of death worldwide. Heart Rate, Blood Pressure, Respiratory Rate, Oxygen Saturation, Systolic Upstroke Time, Heart Beat duration, Diastolic time, RR intervalare some important physiological parameters that help to monitor our daily health condition. Those parameters are very useful to determine if a person is suffering from any cardiovascular problems or not based on daily data collection and monitoring over a certain period of time and in this context machine learning algorithms will be very helpful for developing a smart cardiovascular tele-monitoring & recommendation system for better lifestyle. Irregularities in the heart signal can pop up a serious indication for upcoming cardiac problem. Here, we have concentrated on intensity variation based heart rate calculation process from PPG with major analysis on captured contact video. Here we have used normal handy smart phone camera which is available to everyone and able to capture fingertip videos of flowing blood in the vessels with visible light wavelength. In this paper, we have performed analysis on captured videos for accurate health parameter capturing and compared it with standard devices (FDA approved).
We developed a framework to analyze ant behavior that tracks individual ants and extracts measurements and statistics from the video sequence of a foraging experiment. Fig. 2 shows the pipeline, or sequence of processing steps, of the framework. The first task in the pipeline was to equalize its greyscale histograms with respect to the first frame of the sequence. In addition, registration of each frame against the original frames was performed to discard any slight movement of the bridge of the experiment. This step compensated for any variations in illumination and position during the ex- periment. Illumination correction is particularly important as conditions were not constant. In the second step, the frames were converted to greyscale and the foreground pixels, which correspond to the ants, were segmented by intensity. In the last two steps, the tracking and measurement extraction were performed by allocating each segmented blobs as one ant. No
Frame compatible (FC) formats allow utilization of existing infrastructure and equipment for transmission and services for 3D videos. This format has one video sequence with frame rate f that is the same as in the underlying temporal format. FC formats have lower spa- tial resolution than the underlying spatial format. For example, for the most widely used FC format, the SBS format, frames are spatially sub-sampled in horizontal direction. For instance, for full HD 1, 920 × 1, 080 resolu- tion, the left and the right views of the SBS format have 960 × 1, 080 pixel frames. These sub-sampled frames are interleaved into one frame in full HD resolution. As in the case of the FS format, SBS representation also uses conventional single-view video encoder for coding. 3D video traces
In the literature, the accuracy of a proposed video traffic model is evaluated by testing how close the distributions of the generated I, B and P frame sizes are to those in the original frame trace, which was used to develop the model. This is done using Q-Q plots. A Q-Q plot (Q stands for quantile) is a graphical method for comparing two probability distributions by plotting their quantiles against each other. The Autocorrelation Function (ACF) of the size of successive frames is also confirmed by comparing it with the original one. These comparisons are visual but they do provide some idea of the model’s accuracy. However, they do not reveal any information about the QoS metrics of the model’s resulting packet trace as the flow of packets is transmitted through a series of network elements such as switches and routers. In the literature, this is validated by using the generated trace and the original trace in a single server queue, so that to observe and compare the packet loss rate. The analysis of this single server queue is done by simulation. This type of validation was influenced by studies in the 90s where the video was transmitted over an ATM network. The main performance criterion for congestion control and provision in ATM networks was the cell loss rate. However, currently, video is transmitted over the IP network, and in addition to packet loss, the one-way end-to- end delay and jitter, are also important QoS metrics. Consequently, this validation should be done within the context of a tandem queueing network, which better depicts the path of an end-to-end video flow, with a view to measuring the above three QoS metrics.
Alfonso Sandoval Rosas et. al define the meeting(video session), i.e. system oriented and gives users higher understandability to communicate by conve standard telephony and multimedia in real-time on web browsers. This research proposes a cooperative interaction scheme that differs from others in the context of telephone network communication straight from a web browser during an active video conference, which also enables Real-Time Media Streams between these two techniques to be exchanged.
Simulation environments can be very useful for exploring “what-if” scenarios in complex networking environments that would be difficult to create in a lab environment. In this paper, a Java-based simulation environment was created to explore corner case scenarios within Cable- based IP Video delivery systems. In particular, the authors attempted to identify various mixes of HFC network conditions, IP Video client configurations, and Web-surfing user configurations that might yield “unexpected gotcha’s”- i.e., conditions and configurations that might ultimately lead to undesirable Quality of Experience levels for either the IP Video clients or for the Web-surfing users. Particular focus was given to scenarios that might result in unfairness, where one set of subscribers might be experiencing excellent Quality of Experience levels while a different set of subscribers might be experiencing terrible Quality of Experience levels.
In the last years, the development of novel video coding technologies has spurred the interest in developing digital video communications. The definition of evaluation mechanisms to assess the quality of video will play a major role in the overall design of video communication systems. It is well-known that simple energy based metrics such as the Peak Signal Noise Ratio (PSNR) are not suitable to describe the subjective degradation perceived by a viewer. Recently, new video quality metrics have been proposed in the literature that emulates human perception of video quality since it produces results which are similar to those obtained from the subjective methods. The new models have higher prediction accuracy than the PSNR method, produce consistent results in the range of the data from the subjective tests and are stable across a varying range of video sequences. In this paper, we analyze the capabilities of these new quality measures when are applied to the most popular Hypothetical Reference Circuits (HRC) such as: video compression standards, bit-error transmissions and packet losses.
Any typical indoor or outdoor video taken from either a stationary or moving camera will have objects on background and moving foreground objects. For e.g. a car moving on the road contains the moving car as the foreground objects and road or trees or houses in the background. So for a video taken with a panning camera both the background and foreground objects will be in relative motion. After finding the amount and direction of the shift in the static background objects the relative velocity of the moving camera can be estimated. Hence by focusing only on the objects in the video the shift in the camera can be obtained. Objects in an image can be highlighted by finding the edges in the image since edges represents boundaries of the object in the image. For the objects having different color than the background, the edge corresponds to the boundary between the object and the background or different part of the same object. Hence an edge indicates a sudden jump in intensity from one pixel to another pixel in the direction perpendicular to the edge. Hence an edge indicates a sharp contrast in the intensity .
As stated in the research of Wenjing Wang from the University of Central Florida Orlando in the Vehicular Technology Conference in the year 2007. Live video streaming over vehicular ad hoc networks (VANET) is an attractive feature to many applications, such as emergency live video transmission, road-side video advertisement broadcasting and inter-vehicle video conversation. Though vehicles have ample bandwidth, computation and storage capacity to support data intensive communication, the high mobility may cause persistent network partition. The performance of video streaming suffers from the delay and packet loss incurred by the long-time disconnection. Although many solutions have been proposed to handle the high mobility problem, few of them addressed the problem in the context of video transmission. In this paper, we focus on video streaming between vehicles in highway, where the traffic density is adequate to mitigate frequent link
A recent development in the controversy over children’s use of technology is the attempt to re- habilitate the image of video games. Long criti- cized for their violent content and for monopolizing children’s free time, video games are now being de- fended not just as harmless entertainment but as positive educational experiences for youth. And the defense is coming not just from the video game in- dustry and its enthusiasts but from university pro- fessors as well. As a result, newspaper and magazine articles reassure worried moms and dads that video games are among the things that once were thought to be bad for kids but really are good. Books with ti- tles such as How Computer Games Help Children Learn and Don’t Bother Me, Mom – I’m Learning go farther, portraying video games as essential models of learn- ing that are particularly relevant for 21st century youth. Typically missing from these promotional tomes is any critical analysis of the claims. This essay is an effort to provide such an analysis.