A new Video Synopsis Based Approach Using Stereo Camera

In today's world, the amount of data produced in every field has increased at an unexpected level. In the face of increasing data, the importance of data processing has increased remarkably. Our resource topic is on the processing of video data, which has an important place in increasing data, and the production of summary videos. Within the scope of this resource, a new method for anomaly detection with object-based unsupervised learning has been developed while creating a video summary. By using this method, the video data is processed as pixels and the result is produced as a video segment. The process flow can be briefly summarized as follows. Objects on the video are detected according to their type, and then they are tracked. Then, the tracking history data of the objects are processed, and the classifier is trained with the object type. Thanks to this classifier, anomaly behavior of objects is detected. Video segments are determined by processing video moments containing anomaly behaviors. The video summary is created by extracting the detected video segments from the original video and combining them. The model we developed has been tested and verified separately for single camera and dual camera

[1]  Xiufeng Liu,et al.  Regression-based Online Anomaly Detection for Smart Grid Data , 2016, ArXiv.

[2]  Robert Laganière,et al.  Video summarization of surveillance cameras , 2016, 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[3]  Wei-Ta Chu,et al.  Editing by Viewing: Automatic Home Video Summarization by Viewing Behavior Analysis , 2011, IEEE Transactions on Multimedia.

[4]  F. Diebold.  On the Origin(s) and Development of "Big Data": The Phenomenon, the Term, and the Discipline , 2020 .

[5]  Theodora A. Varvarigou,et al.  Video summarization guiding evaluative rectification for industrial activity recognition , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[6]  Mehmet Serdar Guzel,et al.  A Novel Action Recognition Framework Based on Deep-Learning and Genetic Algorithms , 2020, IEEE Access.

[7]  Anil C. Kokaram,et al.  Discrete wavelet packet transform and ensembles of lazy and eager learners for music genre classification , 2006, Multimedia Systems.

[8]  Zhi-Hua Zhou,et al.  Multi-View Video Summarization , 2010, IEEE Transactions on Multimedia.

[9]  Yi He,et al.  Fast Online Video Synopsis Based on Potential Collision Graph , 2017, IEEE Signal Processing Letters.

[10]  Diego Klabjan,et al.  Classification-Based Financial Markets Prediction Using Deep Neural Networks , 2016, Algorithmic Finance.

[11]  Dim P. Papadopoulos,et al.  Video Summarization Using a Self-Growing and Self-Organized Neural Gas Network , 2011, MIRAGE.

[12]  Amit K. Roy-Chowdhury,et al.  Sparse modeling for topic-oriented video summarization , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Shi-Min Hu,et al.  Visual storylines: Semantic visualization of movie sequence , 2012, Comput. Graph..

[14]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[15]  Muhammad Shakir,et al.  Video Summarization: Techniques and Classification , 2012, ICCVG.

[16]  Tareq Abed Mohammed,et al.  Understanding of a convolutional neural network , 2017, 2017 International Conference on Engineering and Technology (ICET).

[17]  Sujata Chaudhari,et al.  Yolo Real Time Object Detection , 2020 .

[18]  Mariusz Duplaga,et al.  Algorithm for Video Summarization of Bronchoscopy Procedures , 2011, Biomedical engineering online.

[19]  Mehmet Serdar Guzel.  Performance evaluation for feature extractors on street view images , 2016 .

[20]  Feng Liu,et al.  Online video synopsis method through simple tube projection strategy , 2016, 2016 8th International Conference on Wireless Communications & Signal Processing (WCSP).

[21]  Debi Prosad Dogra,et al.  Summarization of Neonatal Video EEG for Seizure and Artifact Detection , 2011, 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics.

[22]  Yale Song,et al.  Video co-summarization: Video summarization by visual co-occurrence , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Wayne H. Wolf,et al.  Key frame selection by motion analysis , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[24]  Weisi Lin,et al.  Scene-Based Movie Summarization Via Role-Community Networks , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[25]  Pau-Choo Chung,et al.  Online surveillance video synopsis , 2012, 2012 IEEE International Symposium on Circuits and Systems.

[26]  Hayri Sever,et al.  Silhouette Extraction from Street View Images , 2014 .

[27]  James M. Rehg,et al.  Gaze-enabled egocentric video summarization via constrained submodular maximization , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Amit K. Roy-Chowdhury,et al.  Diversity-Aware Multi-Video Summarization , 2017, IEEE Transactions on Image Processing.

[29]  Francis X. Diebold,et al.  A Personal Perspective on the Origin(s) and Development of 'Big Data': The Phenomenon, the Term, and the Discipline, Second Version , 2012 .

[30]  Yang Yi,et al.  Key frame extraction based on visual attention model , 2012, J. Vis. Commun. Image Represent..

[31]  Jan-Michael Frahm,et al.  Hysteroscopy video summarization and browsing by estimating the physician's attention on video segments , 2012, Medical Image Anal..

[32]  Yael Pritch,et al.  Making a Long Video Short: Dynamic Video Synopsis , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[33]  Aboul Ella Hassanien,et al.  SVM-based soccer video summarization system , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[34]  Chun-Rong Huang,et al.  Maximum a Posteriori Probability Estimation for Online Surveillance Video Synopsis , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[35]  Markus Schedl,et al.  The effect of different video summarization models on the quality of video recommendation based on low-level visual features , 2017, CBMI.

[36]  Priyanka Sharma,et al.  Survey of Compressed Domain Video Summarization Techniques , 2019, ACM Comput. Surv..