Efficient Video Summarization Based on Motion SIFT-Distribution Histogram

in the context of mobile computing and multimedia processing, video summarization plays an important role for video browsing, streaming, indexing and storing. In this paper, an effective and efficient video summarization approach for mobile devices is proposed. The goal of this approach is to generate a video summary (static and dynamic) based on Visual Attention Model (VAM) and new Fast Directional Motion Intensity Estimation (FDMIE) algorithm for mobile devices. The VAM is based on how to simulate the Human Vision System (HVS) to extract the salient areas that have more attention values from video contents. The evaluation results demonstrate that, the effectiveness rate up to 87% with respect to the manually generated summary and the state of the art approaches. Moreover, the efficiency of the proposed approach makes it suitable for online and mobile applications.

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Multimodal Summarization of User-Generated Videos

Applied Sciences ◽

10.3390/app11115260 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5260

Author(s):

Theodoros Psallidas ◽

Panagiotis Koromilas ◽

Theodoros Giannakopoulos ◽

Evaggelos Spyrou

Keyword(s):

Exponential Growth ◽

Temporal Order ◽

Video Summarization ◽

User Generated Content ◽

Visual Features ◽

Original Video ◽

Binary Classifier ◽

Video Summaries ◽

Audio Video ◽

Efficient Video

The exponential growth of user-generated content has increased the need for efficient video summarization schemes. However, most approaches underestimate the power of aural features, while they are designed to work mainly on commercial/professional videos. In this work, we present an approach that uses both aural and visual features in order to create video summaries from user-generated videos. Our approach produces dynamic video summaries, that is, comprising the most “important” parts of the original video, which are arranged so as to preserve their temporal order. We use supervised knowledge from both the aforementioned modalities and train a binary classifier, which learns to recognize the important parts of videos. Moreover, we present a novel user-generated dataset which contains videos from several categories. Every 1 sec part of each video from our dataset has been annotated by more than three annotators as being important or not. We evaluate our approach using several classification strategies based on audio, video and fused features. Our experimental results illustrate the potential of our approach.

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An optimal video summarization of surveillance systems using LFOB-COA with deep features and RBLSTM

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212800 ◽

2022 ◽

pp. 1-9

Author(s):

D. Minola Davids ◽

C. Seldev Christopher

Keyword(s):

Video Summarization ◽

Original Video ◽

Surveillance Systems ◽

Single Camera ◽

Major Task ◽

Shot Segmentation ◽

Deep Feature ◽

Processing Step ◽

Deep Feature Extraction ◽

Efficient Video

The visual data attained from surveillance single-camera or multi-view camera networks is exponentially increasing every day. Identifying the important shots in the presented video which faithfully signify the original video is the major task in video summarization. For executing efficient video summarization of the surveillance systems, optimization algorithm like LFOB-COA is proposed in this paper. Data collection, pre-processing, deep feature extraction (FE), shot segmentation JSFCM, classification using Rectified Linear Unit activated BLSTM, and LFOB-COA are the proposed method’s five steps. Finally a post-processing step is utilized. For recognizing the proposed method’s effectiveness, the results are then contrasted with the existent methods.

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