Dominant and rare events detection and localization in video using Generative Adversarial Network

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Khalooei Mohammad; fakhredanesh mohammad; Sabokrou Mohammad

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT) Year:2019 | Volume:8 | Issue:3 Page(s): 40-51

Download Full-Text

Persian Verion

View:

461

Download:

Cites:

Information Journal Paper

Title

Dominant and rare events detection and localization in video using Generative Adversarial Network

Author(s)

Khalooei Mohammad | fakhredanesh mohammad | Sabokrou Mohammad | Issue Writer Certificate

Keywords

Detecting rare eventQ1

Generative adversarial networkQ1

anomaly detectionQ1

anomaly localizationQ1

Abstract

Dominant and rare events detection is one of the most important subjects of image and video analysis field. Due to inaccessibility to all rare events, detecting of them is a challenging task. Today, deep networks are the best tool for video modeling but due to inaccessibility to tagged data of rare data, usual learning of a deep convolutional network is not possible. Due to the success of Generative adversarial networks, in this paper an end-to-end deep network based on Generative adversarial networks is presented for Detecting rare events. This network is competitively trained only by dominant events. To evaluate performance of proposed method, two standard datasets: UCSDped1 and UCSDped2 are utilized. The proposed method can detect rare event with 0. 2 and 0. 17 equal error rate with the processing speed of 300 frames per second on the mentioned data respectively. In addition to end-to-end structure of the network and its simple train and test phase, this result is comparable to advanced methods results.

Cites

No record.

References

No record.

Cite

APA: Copy

Khalooei, Mohammad, fakhredanesh, mohammad, & Sabokrou, Mohammad. (2019). Dominant and rare events detection and localization in video using Generative Adversarial Network. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), 8(3 ), 40-51. SID. https://sid.ir/paper/245851/en

Vancouver: Copy

Khalooei Mohammad, fakhredanesh mohammad, Sabokrou Mohammad. Dominant and rare events detection and localization in video using Generative Adversarial Network. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT)[Internet]. 2019;8(3 ):40-51. Available from: https://sid.ir/paper/245851/en

IEEE: Copy

Mohammad Khalooei, mohammad fakhredanesh, and Mohammad Sabokrou, “Dominant and rare events detection and localization in video using Generative Adversarial Network,” JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), vol. 8, no. 3 , pp. 40–51, 2019, [Online]. Available: https://sid.ir/paper/245851/en

Related Journal Papers