Using Natural Language and Video Data to Query and Learn American Football Plays

Natural Language Description of Videos for Smart Surveillance

Applied Sciences ◽

10.3390/app11093730 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3730

Author(s):

Aniqa Dilawari ◽

Muhammad Usman Ghani Khan ◽

Yasser D. Al-Otaibi ◽

Zahoor-ur Rehman ◽

Atta-ur Rahman ◽

...

Keyword(s):

Natural Language ◽

Feature Recognition ◽

Scene Recognition ◽

Video Data ◽

Surveillance Video ◽

Video Footage ◽

Parallel Pipeline ◽

September 11 Attacks ◽

Description Framework ◽

High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

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Combining NL processing and video data to query American Football

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170) ◽

10.1109/icpr.1998.711923 ◽

2002 ◽

Cited By ~ 3

Author(s):

M. Lazarescu ◽

S. Venkatesh ◽

G. West ◽

T. Caelli

Keyword(s):

Video Data ◽

American Football

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Generating Highlights of Cricket Video using Commentators and Spectators Voice

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4261.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 10134-10136

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Short Duration ◽

Language Processing ◽

Digital Technology ◽

Video Summarization ◽

Video Data ◽

Experimental Results ◽

The Voice

Videos are one of the important and richest sources of data on internet. In this growing world of digital technology video summarization will be handy in analysing the video data. Recently Natural Language Processing has attracted more researchers to work to meet the current emerging challenges. Among the various issues, video summarization got more focus and in this regard, many applications and works have been evolved. Video Summarization is the process of creating a small video describing the actual video within short duration(s). The paper focuses on generating highlights of a cricket video by analysing the voice of commentator and spectators. The experimental results have shown good performance when compared with human generated summary.

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