Subject of study. In this paper, for the first time, an original method for estimating the change in the brightness of video data under the influence of changes in the lighting conditions of the ...scene and external noise is proposed. Algorithms for stabilizing the brightness of video data are also proposed. An objective assessment of the quality of video data pre-processed is given. The purpose of the research is to create a methodology for analyzing the variability of video data parameters under the influence of negative factors and to develop effective algorithms for stabilizing the parameters of the received video stream. The reliability of the method is tested using real video recordings pictured through various conditions. Objectives: To determine the most universal, resistant to external influences, and informative indicator necessary for an objective assessment of the quality of video data under various shooting conditions and scene lighting features; develop and programmatically implement algorithms for stabilizing video parameters based on modern programming tools. Research methods. Statistical analysis and pre-processing of video stream parameters as a random spatio-temporal process, algorithms for processing video data by digital filtering, and adaptive stabilization of video stream parameters. Research results. It has been proposed and experimentally proven that the optimal indicator of video stream quality is the average frame brightness (AFB). An algorithm for spatiotemporal processing of video data is proposed that generates a sequence of AFB values from the original video stream. The paper also proposes digital algorithms for filtering and stabilizing the brightness of a video stream and investigates the effectiveness of their application. Conclusions. The scientific novelty of the results obtained lies in a new method for analyzing and evaluating the parameters of video surveillance data and algorithms for filtering and stabilizing the brightness of the video stream. The performance of the proposed algorithms has been tested on real data. The algorithms are implemented in the Python software environment using the functions of the OpenCV library.
•We build a dataset, which includes video and time-sync comments, for segment popularity prediction.•We study the problem of video segment popularity prediction, which can help audiences to find ...attractive shots.•With the help of time-sync comments information mining, the model can better understanding the audiences.•Combining visual and text information can make results more precise.
With the rapid development of digital equipment and the continuous upgrading of online media, a growing number of people are willing to post videos on the web to share their daily lives (Jelodar, Paulius, & Sun, 2019). Generally, not all video segments are popular with audiences, some of which may be boring. If we can predict which segment in a newly generated video stream would be popular, the audiences can only enjoy this segment rather than watch the whole video to find the funny point. And if we can predict the emotions that the audiences would induce when they watch a video, this must be helpful for video analysis and for guiding the video-makers to improve their videos. In recent years, crowd-sourced time-sync video comments have emerged worldwide, supporting further research on temporal video labeling. In this paper, we propose a novel framework to achieve the following goal: Predicting which segment in a newly generated video stream (hasn’t been commented with the time-sync comments) will be popular among the audiences. At last, experimental results on real-world data demonstrate the effectiveness of the proposed framework and justify the idea of predicting the popularities of segments in a video exploiting crowd-sourced time-sync comments as a bridge to analyze videos.
With the pioneering introduction of autonomous vehicles, system failures while driving from A to B are more likely to occur. In such scenarios one option is to hand back the control to the human ...driver, if someone suitable is inside the vehicle. Teleoperated Driving, the remote control of vehicles by human operators, can be a solution to scenarios without suitable drivers inside. A video stream is used to provide operators with an overview of the vehicle's environment and support for a safe remote control. By utilizing cellular networks as wireless communication medium for Teleoperated Driving, the available bandwidth is a limiting factor. This paper introduces a multi-step approach to lower the bandwidth requirements, which is achieved by initially splitting the single video stream into two parts: One part conveying the original video information restricted to important objects and the remainder, to which various filters are applied. Results show that this approach can lead to a decreased bandwidth consumption. These results are validated with a user study, where participants had to rate the perceived video quality and the driveability for the different combinations. This user study shows that, for every investigated scenario, at least one combination of parameters (applied filters) was rated driveable. Finally, the results are used to sketch a system that infers specific combinations of parameters based on the environmental conditions and the available bitrate.
In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task ...of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.
The article presents an approach to the control of a UAV on the basis of 3D landmark observations. The novelty of the work is the usage of the 3D RANSAC algorithm developed on the basis of the ...landmarks' position prediction with the aid of a modified Kalman-type filter. Modification of the filter based on the pseudo-measurements approach permits obtaining unbiased UAV position estimation with quadratic error characteristics. Modeling of UAV flight on the basis of the suggested algorithm shows good performance, even under significant external perturbations.
In contrast to traditional video, multi-view video streaming allows viewers to interactively switch among multiple perspectives provided by different cameras. One approach to achieve such a service ...is to encode the video from all of the cameras into a single stream, but this has the disadvantage that only a portion of the received video data will be used, namely that required for the selected view at each point in time. In this paper, we introduce the concept of a "multi-video stream bundle" that consists of multiple parallel video streams that are synchronized in time, each providing the video from a different camera capturing the same event or movie. For delivery we leverage the adaptive features and time-based chunking of HTTP-based adaptive streaming, but now employing adaptation in both content and rate. Users are able to change their viewpoint on-demand and the client player adapts the rate at which data are retrieved from each stream based on the user's current view, the probabilities of switching to other views, and the user's current bandwidth conditions. A crucial component of such a system is the prefetching policy. For this we present an optimization model as well as a simpler heuristic that can balance the playback quality and the probability of playback interruptions. After analytically and numerically characterizing the optimal solution, we present a prototype implementation and sample results. Our prefetching and buffer management solution is shown to provide close to seamless playback switching when there is sufficient bandwidth to prefetch the parallel streams.
Many existing methods for frame deletion detection attempt to detect abnormal periodical artifacts in video stream, however, due to a number of reasons, the periodical artifacts can not always be ...reliably detected. In this paper, we propose a new method for frame deletion detection. Rather than detecting abnormal periodical artifacts, we devise two features to measure the magnitude of variation in prediction residual and the number of intra macro blocks. Based on the devised features, we propose a fused index to capture abnormal abrupt changes in video streams. We create a dataset which consists of 6 subsets, and test the detection capability of our method in both video level and GOP (Group of Pictures) level. The experimental results show that the proposed method performs stably under various configurations.
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