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Video Summarization Using Highlight Detection and Pairwise Deep Ranking Model

M. Sridevi, Mayuri Kharde
2020 Procedia Computer Science  
Furthermore, as the highlight result depicts only a relative level of interest of a user in a video, the DCNN in each stream is trained with a pairwise deep ranking model.  ...  generate highlight scores for segments of the video.  ...  Fig. 2 .Fig. 3 . 23 Overall Block diagram for video highlight detection Fig. 4 . 4 Block diagram for Pairwise Ranking Model  327  327 Without Pairwise Ranking Model: o Precision for spatial stream  ... 
doi:10.1016/j.procs.2020.03.203 fatcat:n3h32rudx5d7rkm5orazo6hqjy

Highlight Detection with Pairwise Deep Ranking for First-Person Video Summarization

Ting Yao, Tao Mei, Yong Rui
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Specifically, we propose a novel pairwise deep ranking model that employs deep learning techniques to learn the relationship between highlight and non-highlight video segments.  ...  Given a long personal video, equipped with the highlight detection model, a highlight score is assigned to each segment.  ...  Pairwise Deep Ranking Model As with most deep learning problems, the learning of our spatial and temporal DCNN architectures are critical for video highlight detection.  ... 
doi:10.1109/cvpr.2016.112 dblp:conf/cvpr/YaoMR16 fatcat:gqpvseiow5dinp2zzuifjhpnse

MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection [article]

Fa-Ting Hong, Xuanteng Huang, Wei-Hong Li, Wei-Shi Zheng
2020 arXiv   pre-print
In this work, we propose casting weakly supervised video highlight detection modeling for a given specific event as a multiple instance ranking network (MINI-Net) learning.  ...  We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but without expensive supervision  ...  .: Highlight detection with pairwise deep ranking for first-person video summarization. In: Computer Vision and Pattern Recognition (2016) 36.  ... 
arXiv:2007.09833v2 fatcat:i66exkby2fbejmir2jftgfcsqm

Spatial–Temporal Attention Two-Stream Convolution Neural Network for Smoke Region Detection

Zhipeng Ding, Yaqin Zhao, Ao Li, Zhaoxiang Zheng
2021 Fire  
Smoke detection is of great significance for fire location and fire behavior analysis in a fire video surveillance system.  ...  Smoke image classification methods based on a deep convolution network have achieved high accuracy.  ...  A joint detection framework based on fast R-CNN and 3D-CNN (three-dimensional CNN) was developed to realize smoke target localization [17] .  ... 
doi:10.3390/fire4040066 fatcat:rwaj3omg2jekfmhig23lgempxq

A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos

Zheng Wang, Jinchang Ren, Dong Zhang, Meijun Sun, Jianmin Jiang
2018 Neurocomputing  
2018) A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing.  ...  In addition, the proposed framework is found useful for other video content based applications such as video highlights.  ...  Acknowledgements The authors would greatly thank the Editors and anonymous reviewers for their constructive comments to further improve the clarity and quality of this paper.The authors wish to acknowledge  ... 
doi:10.1016/j.neucom.2018.01.076 fatcat:nfzjix4kjzgihe5jw3wz2r5jta

Research on Salient Object Detection using Deep Learning and Segmentation Methods

2019 International journal of recent technology and engineering  
It not only focuses on the methods to detect saliency objects, but also reviews the works related to spatio temporal video attention detection technique in video sequences.  ...  While many models have been proposed and several applications have emerged, yet a deep understanding of achievements and issues is lacking.  ...  The spatial and the temporal saliency maps are constructed and further fused together to create a novel attention model. The attention model is evaluated on three video datasets.  ... 
doi:10.35940/ijrte.b1046.0982s1119 fatcat:6ofq53vb7zhx7boq4ndpraphs4

Global and Local Sensitivity Guided Key Salient Object Re-augmentation for Video Saliency Detection [article]

Ziqi Zhou, Zheng Wang, Huchuan Lu, Song Wang, Meijun Sun
2018 arXiv   pre-print
Results on three benchmark datasets suggest that our model has the capability of improving the detection accuracy on complex scenes.  ...  The existing still-static deep learning based saliency researches do not consider the weighting and highlighting of extracted features from different layers, all features contribute equally to the final  ...  [15] also designed a video saliency detection model based on similar ideas. [3] proposed a video saliency detection model based on multi-stream convLSTM.  ... 
arXiv:1811.07480v1 fatcat:xeypmi5u7zhzpay7s2rplnxcry

Crowd understanding and analysis

Qi Wang, Bo Liu, Jianzhe Lin
2021 IET Image Processing  
These social activities are often attended by a wide range of people, which puts forward high requirements for effective management and ensures the safety of the people involved in the activities.  ...  The basic idea is to extract the key information from video sequences/images, and use digital image processing technology to study and analyse the behaviour characteristics and patterns of people in the  ...  "Anomaly detection in video sequences: a benchmark and computational model" of Wan et al. contributes a new Largescale Anomaly Detection (LAD) dataset as the benchmark for anomaly detection in video sequences  ... 
doi:10.1049/ipr2.12379 fatcat:shshhjjoxngotplvg7xzefpsne

Indirect Match Highlights Detection with Deep Convolutional Neural Networks [article]

Marco Godi, Paolo Rota, Francesco Setti
2017 arXiv   pre-print
Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience.  ...  We apply deep 3D Convolutional Neural Network (3D-CNN) to extract visual features from cropped video recordings of the supporters that are attending the event.  ...  For our model we used a sliding window with a stride of 50 pixels resulting in a maximum overlap between two crops of 50% In order to detect and rank the most important moments in the video sequence we  ... 
arXiv:1710.00568v1 fatcat:zip2quma6feafmws4usrpsuylq

Find Objects and Focus on Highlights: Mining Object Semantics for Video Highlight Detection via Graph Neural Networks

Yingying Zhang, Junyu Gao, Xiaoshan Yang, Chang Liu, Yan Li, Changsheng Xu
Secondly, current state-of-the-art approaches often adopt the pairwise ranking-based strategy, which cannot enjoy the global information to infer highlights.  ...  Firstly, most existing approaches only focus on learning holistic visual representations of videos but ignore object semantics for inferring video highlights.  ...  propose DCNN and employ deep learning techniques to learn the relationship between highlight and non-highlight video segments with a pairwise deep ranking model (Yao, Mei, and Rui 2016) .  ... 
doi:10.1609/aaai.v34i07.6988 fatcat:biaanlopgvbw3nbt44niavse2m

Personalized Video Summarization Based Exclusively on User Preferences [chapter]

Costas Panagiotakis, Harris Papadakis, Paraskevi Fragopoulou
2020 Lecture Notes in Computer Science  
We propose a recommender system to detect personalized video summaries, that make visual content interesting for the subjective criteria of the user.  ...  In order to provide accurate video summarization, the video segmentation provided by the users and the features of the video segments' duration are combined using a Synthetic Coordinate based Recommendation  ...  In [19] , a novel pairwise deep ranking model is proposed that employs deep learning in order to learn the relationship between highlighted and non-highlighted video segments.  ... 
doi:10.1007/978-3-030-45442-5_38 fatcat:4s7cd7zqmbdfzm6wqae6lkk27m

Low Quality Video Face Recognition: Multi-Mode Aggregation Recurrent Network (MARN)

Sixue Gong, Yichun Shi, Anil Jain
2019 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)  
For low quality video sequences, however, more discriminative features can be obtained by aggregating the information in video frames.  ...  Compared with quality-aware aggregation methods, MARN utilizes the video context and learns multiple attention vectors adaptively.  ...  The red boxes are used to highlight the ground truth identity. Since faces are difficult to detect in UAV videos, we only show examples for four of the five protocols.  ... 
doi:10.1109/iccvw.2019.00132 dblp:conf/iccvw/GongSJ19 fatcat:hk27qw4tz5d4hkiecoyiblmbrq

A Survey on various Video Summarization Techniques

2021 International Journal of Advanced Trends in Computer Science and Engineering  
In this fast-moving world people don't have the time to watch lengthy videos, so it would be convenient for them if they could access short summaries of these videos and be able to acquire more information  ...  A study of the different methods used for video summarization over the years i.e., extracting important segments from a video to produce short concise summaries that are representative of the original  ...  This method ranks 24th for redundant frame inclusion and ranks 39 for inclusion criteria.  ... 
doi:10.30534/ijatcse/2021/1011022021 fatcat:m3prnrymjrbl7eurjk2kzdjisy

Pose-Guided Multi-Scale Structural Relationship Learning for Video-Based Pedestrian Re-Identification

Dan Wei, Xiaoqiang Hu, Ziyang Wang, Jianglin Shen, Hongjuan Ren
2021 IEEE Access  
How to extract discriminative features from redundant video information is a key issue for video pedestrian re-identification.  ...  The purpose is to analyze the video sequence of pedestrians based on the reference pose and the pose alignment model, and extract the sample frame with the highest image quality and the most complete spatial  ...  , and aggregate local three-dimensional features across the entire video.  ... 
doi:10.1109/access.2021.3062967 fatcat:rec42qjynbg7dhpbfvvwy7t3ja

Deep anomaly detection through visual attention in surveillance videos

Nasaruddin Nasaruddin, Kahlil Muchtar, Afdhal Afdhal, Alvin Prayuda Juniarta Dwiyantoro
2020 Journal of Big Data  
The resulting regions are finally fed into a three-dimensional Convolutional Neural Network (3D CNN).  ...  This paper describes a method for learning anomaly behavior in the video by finding an attention region from spatiotemporal information, in contrast to the full-frame learning.  ...  A novel localization idea for a deep learning network to learn anomaly scores for video segments is introduced. This paper is organized as follows: In section II, we present related works.  ... 
doi:10.1186/s40537-020-00365-y fatcat:cn2dsuzxlrg7db3qyqlxqihvia
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