Proceedings Article10.1109/CVPRW.2018.00233
Automatic Cricket Highlight Generation Using Event-Driven and Excitement-Based Features
Pushkar Shukla,Hemant Sadana,Apaar Bansal,Deepak Verma,Carlos Elmadjian,Balasubramanian Raman,Matthew Turk +6 more
- 18 Jun 2018
- pp 1800-1808
TL;DR: A model capable of automatically generating sports highlights with a focus on cricket is proposed that considers both event-based and excitement-based features to recognize and clip important events in a cricket match.
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Abstract: Producing sports highlights is a labor-intensive work that requires some degree of specialization. We propose a model capable of automatically generating sports highlights with a focus on cricket. Cricket is a sport with a complex set of rules and is played for a longer time than most other sports. In this paper we propose a model that considers both event-based and excitement-based features to recognize and clip important events in a cricket match. Replays, audio intensity, player celebration, and playfield scenarios are examples of cues used to capture such events. To evaluate our framework, we conducted a set of experiments ranging from user studies to a comparison analysis between our highlights and the ones provided by the official broadcasters. The general approval by users and the significant overlap between both kinds of highlights indicate the usefulness of our model in real-life scenarios.
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References
Video Summarization with Long Short-Term Memory
Ke Zhang,Wei-Lun Chao,Fei Sha,Kristen Grauman +3 more
- 08 Oct 2016
TL;DR: In this paper, the task of video summarization is cast as a structured prediction problem, and LSTM is used to model the variable-range temporal dependency among video frames to derive both representative and compact video summaries.
663
•Posted Content
Video Summarization with Long Short-term Memory
TL;DR: Long Short-Term Memory (LSTM), a special type of recurrent neural networks are used to model the variable-range dependencies entailed in the task of video summarization to improve summarization by reducing the discrepancies in statistical properties across those datasets.
598
Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions
Sven Bambach,Stefan Lee,David J. Crandall,Chen Yu +3 more
- 07 Dec 2015
TL;DR: This work develops methods to locate and distinguish between hands in egocentric video using strong appearance models with Convolutional Neural Networks, and introduces a simple candidate region generation approach that outperforms existing techniques at a fraction of the computational cost.
Action Recognition in Realistic Sports Videos
Khurram Soomro,Amir Roshan Zamir +1 more
- 01 Jan 2014
TL;DR: This chapter provides a detailed study of the prominent methods devised for action localization and recognition in videos and argues that performing the recognition on temporally untrimmed videos and attempting to describe an action, instead of conducting a forced-choice classification, are essential for analyzing the human actions in a realistic environment.
A Survey of Content-Aware Video Analysis for Sports
TL;DR: This paper focuses on the video content analysis techniques applied in sportscasts over the past decade from the perspectives of fundamentals and general review, a content hierarchical model, and trends and challenges.
251