A Study on Keyframe Extraction Methods for Video Summary

An efficient video summarization for surveillance system using normalized k-means and quick sort method

Objective measures of performance are seldom perfect. In response, incentive contracts often include important subjective components that mitigate incentive distortions caused by imperfect objective measures. This paper explores the combined use of subjective and objective performance measures in (respectively) implicit and explicit incentive contracts. Naturally, objective and subjective measures often are substitutes, sometimes strikingly so: we show that if objective measures are sufficiently close to perfect then no implicit contracts are feasible (because the firm's fallback position after reneging on an implicit contact is too attractive). We also show, however, that objective and subjective measures can reinforce each other: if objective measures become more accurate then in some circumstances the optimal contract puts more weight on subjective measures (because the improved objective measures increase the value of the ongoing relationship, and so reduce the firm's incentive to renege). We also analyze the use of subjective weights on objective performance measures, and provide case-study evidence consistent with our analyses.

Key frame selection, which aims to choose a small number of frames that well represent the entire video contents, is one of the important research issues in video content analysis. Many existing methods developed so far use some prior information about the contents of video frames based on high-level semantic features, and thus they are applicable only in particular problems. Without any prior information, approaches based on low-level index features such as clusters, correlation, and consecutive frame difference are useful. Key frames found by these approaches are expected to be as much different from each other as possible. Therefore, accurately evaluating the difference among candidate key frames is a key challenge. However, a standard similarity metric such as the squared error and the cross correlation tends to provide redundant key frames. To cope with this problem, we propose to use quadratic mutual information (QMI) as an alternative similarity measure, which allows us to capture higher-order correlation for more than two variables simultaneously. Through experiments, we demonstrate the usefulness of the proposed QMI-based key frame selection method that uses an accurate and computationally efficient least-squares QMI estimator.

As video summarization techniques have attracted more and more attention for efficient multimedia data management, quality assessment of video summary is required. To address the lack of automatic evaluation techniques, this paper proposes a novel framework including several new algorithms to assess the quality of the video summary against a given reference. First, we partition the reference video summary and the candidate video summary into the sequences of summary unit (SU). Then, we utilize alignment based algorithm to match the SUs in the candidate summary with the SUs in the corresponding reference summary. Third, we propose a novel similarity based 4 C - assessment algorithm to evaluate the candidate video summary from the perspective of coverage, conciseness, coherence, and context, respectively. Finally, the individual assessment results are integrated according to userpsilas requirement by a learning based weight adaptation method. The proposed framework and techniques are experimented on a standard dataset of TRECVID 2007 and show the good performance in automatic video summary assessment.

Due to video data exponential growth supported by quick advances in multimedia technology, it became difficult for the user to retrieve information from a large videos collection. Key frame extraction consists in providing an abstract of the entire video, containing the most informative frames. In this paper, we present an efficient key frame extraction method. This method is based on local interest points description and repeatability graph clustering via approaching modularity value. To minimize the data to be treated, the process will be applied only on a set candidate frames selected with a windowing rule. Indeed, the first step consists in extracting interest points on a set of candidate frames. After that, we compute repeatability values between each two frames from the candidate set. These values are represented by a repeatability direct graph. The selection of key frames is performed using graph clustering by approaching modularity principle. The experiments performed showed that the proposed method succeeds in extracting key frames that preserve the relevant video content without redundancy.

Subjective versus objective measures of distress, arousal and symptom burden in patients with functional seizures and other functional neurological symptom disorder presentations: A systematic review

Short videos on the Internet are growing exponentially, and the number of videos uploaded every day is huge; people also involve a lot of video data in real life. People can retrieve and view all kinds of videos, but it also brings a lot of problems. On the one hand, the accumulation of a large number of videos makes people unable to find the videos they want quickly, and the repeated scenes in the videos will also waste people's time and energy; on the other hand, a large amount of video data also brings enormous pressure to storage. Aiming at the problems of inaccurate selection of key frames and how to select video frame features in existing video summarization models, this paper proposes a multi-feature-based video summarization generation model (DME-VSNet), which extracts multiple features of video frames. Including importance score, image memory strength and image entropy. Aiming at the problem of inaccurate video shot segmentation, this model proposes a video shot segmentation algorithm based on TransNet network, which divides the original video into several short shots through shot boundaries; the model inputs the above three features into the proposed The video frame score is obtained in the MLP architecture, and the key frame is selected by the score to generate a video summary. The effectiveness of the video shot segmentation method based on TransNet network and the overall model based on convolutional neural network is verified by comparative experiments. The experimental results show that the evaluation results of the video summaries generated by the three features are better.

Based on compressed video segment detection, the decompression step can be omitted, features can be directly extracted from the original video data stream, and the detection rate can be accelerated. This paper analyzes the characteristics of video data, video segmentation and key frame selection. Based on the analysis of the key technologies of video retrieval technology, the compressed video stream extraction method based on video segmentation and key frame is introduced. A method of extracting key frames from MPEG compressed video based on DC coefficients and motion vectors is proposed. Experiments show that the proposed method can reduce the computational burden, and can better represent the video content.

Surface hardness monitoring of laser shock Peening: Acoustic emission and key frame selection

Objective vs Subjective Measurements of Palatine Tonsil Size in Patients with OSA

We present a framework for efficient reconstruction of dense scene structure from video. Sequential structure-from-motion recovers camera information from video, providing only sparse 3D points. We build a dense 3D point cloud by performing full-frame tracking and depth estimation across sequences. First, we present a novel algorithm for sequential frame selection to extract a set of key frames with sufficient parallax for accurate depth reconstruction. Second, we introduce a technique for efficient reconstruction using dense tracking with geometrically correct optimisation of depth and orientation. Key frame selection is also performed in optimisation to provide accurate depth reconstruction for different scene elements. We test our work on benchmark footage and scenes containing local non-rigid motion, foreground clutter and occlusions to show comparable performance to state of the art techniques. We also show a substantial increase in speed on real world footage compared to existing methods, when they succeed, and successful reconstructions when they fail.

BackgroundThe physical and mental demands of handball during training or competition often lead to fatigue which can impair performance. Many attempts have been made to assess the level of fatigue in athletes either by objective (neuromuscular performance) or subjective (questionnaires) measures, however, their interplay over short-, mid-, and long-term periods is currently unknown. Knowledge about both types of assessments is important as load management by coaches is traditionally based on direct adjustments following a training session, adjustments of content structure of training weeks between games, as well as adjustments of load management over the entire competitive season. Thus, this study aimed to investigate the interplay between objective and subjective fatigue measures at multiple test times throughout a handball season.MethodsA total of 100 highly trained (Tier level 3) adolescent or young adult team handball players (23 females) took part in the study. The parameters tested were the Leg Recovery Test (LRT score) which is based on the countermovement jump height (CMJ) and was assessed by a commercial wristwatch (Polar Vantage V2) as an objective measure of neuromuscular fatigue. Additionally, on a subjective level, questionnaire-based athlete self-report measures, specifically the Perceived Recovery Status Scale (PRSS) and the Short Scale of Recovery and Strain (KEB) were assessed. We used non-parametric tests to detect differences between relevant test time points (short-term: immediately following one handball-specific training session, i.e., from T0 to T1; mid-term: over the course of three consecutive training days, i.e., from T0 to T2; long-term: over the course of 8 months of training, i.e., from T0 to T12) and linear mixed models to evaluate the interplay between objective (LRT score) and subjective (KEB score and PRSS score) measures of fatigue across one season.ResultsNon-parametric tests showed that CMJ height (p = .012) and the KEB (p < .001) were higher at T1 compared to T0 for the short-term assessment. Over the course of three consecutive training days (i.e., mid-term assessment), the CMJ height score decreased (T0 to T2: p < .001; T1 to T2: p = .018) and the PRSS score (T0 to T2: p < .001; T1 to T2: p = .003) increased. Linear mixed models revealed no significant effects of KEB or PRSS score on LRT score (i.e., CMJ height) for the short- and mid-term assessments. In terms of the long-term assessments, we detected no general direct or interaction effects of PRSS score, workload, and test time point on LRT score, except for an interaction between PRSS score and workload on LRT score (p = .032), which indicates a workload-dependent association between PRSS and the objective fatigue measure (LRT score).ConclusionAthlete self-reported measures of fatigue indicated significantly higher cumulative fatigue after both short- and mid-term periods, whereas this increase was observed in the LRT score only during the mid-term period. Furthermore, the absence of a relationship between the objective and subjective measures of fatigue during short- and mid-term periods suggests that these measures assess distinct types of fatigue. In the long-term assessments, the significant interaction between the PRSS score and workload on the LRT score suggests that higher workloads are associated with an increased correlation between subjective (PRSS score) and objective (LRT score) measures of fatigue. This indicates that perceived fatigue may be a more sensitive indicator of fatigue, which can be managed to maintain high levels of neuromuscular performance (LRT score). However, with higher workloads (>10 h per week), associations between the objective and subjective measures become apparent, suggesting that workload serves as a common factor influencing overall fatigue.

Subjective and objective measures available at the time of medical school admission were related to subjective and objective clinical performance measures during medical school. Strategies were developed for coding narrative faculty comments from admissions interviews and clinical performance evaluations. Factor analysis was used to examine underlying structures for both admission and clinical performance measures. Summed scores were calculated to represent each factor, and multiple regression was used in predicting each of the clinical factors from the admission factors. Multiple regression showed that admission interview comments best predict narrative clerkship performance, while objective scores best predict an objective measure of clinical knowledge. Conclusions were: (a) narrative information can be coded reliably. (b) Objective and subjective measures are distinct, identifiable structures both at admission and during the third year of medical school. (c) Prediction formulas will vary depending on what outcome variables are chosen.

This study investigated the clinical utility of the combined use of objective and subjective measures of attention-deficit/hyperactivity disorder (ADHD) prepharmacological and postpharmacological treatment. Adults with ADHD (N = 77) completed the Quantified Behavioral Test, self-ratings of ADHD-related symptoms, and quality of life measures pretreatment and posttreatment. The use of objective and subjective measures of ADHD-related symptoms during initiation and follow-up of pharmacological treatment resulted in significant improvements in quality of life after 6 months. Both objective and subjective measures captured changes in ADHD-related symptoms, with more patients showing clinically relevant treatment effects on objective measures. Convergence rates between objective and subjective measures were low to moderate, and improvements on these measures correlated with increased quality of life. Objective and subjective measures of ADHD capture important components of the condition. The findings from this study have important implications for clinical practice.

Shot reconstruction degree: a novel criterion for key frame selection