An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach. Issue 4 (9th April 2021)

• Record Type: Journal Article
• Authors: U., Sreeja M.; Kovoor, Binsu C.
• Is Part Of: IET computer vision. Volume 15:Issue 4(2021)
• Page Start: 297
• Page End: 311
• Publication Date: 2021-04-09
• Subjects: Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/cvi2.12044
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract: Surveillance video summarisation is characterised by extracting video segments containing abnormal events from surveillance video footages. Accurate identification of abnormal events from surveillance footages is of paramount importance in surveillance video summarisation. Accordingly, the proposed framework builds an aggregated convolutional recurrent model that can precisely detect the suspicious events in a surveillance footage, by employing a supervised learning which is found to yield better results compared with unsupervised counterparts. The preliminary stage in the model is a multilayer Convolutional Neural Network for frame‐level feature extraction followed by stacked bidirectional Gated Recurrent Unit for sequence‐level feature extraction and classification. Since the video clips used for training are not implicit to surveillance, a block‐based approach for testing on surveillance videos is proposed. The results evaluated on two custom datasets, Streets and Campus, prove that the proposed model produces remarkable results leveraging the properties of bidirectional GRU with supervised learning. Extensive experimental analysis on selection of optimum architecture is conducted which substantiates the significance of stacked bidirectional GRUs over unidirectional ones. Additionally, qualitative results ensure that summaries produced are concise, representative, complete, diverse and informative. Moreover, comparison of the performance of the proposed model with state of the art certainly proves the superiority of the proposed model.