An Analysis of Business Model Canvas on a Comedy Company (A Study Case of Majelis Lucu Indonesia/PT. Jenaka Sumber Rezeki)

This study aims to determine the business model of a talent development company or comedy company at Majelis Lucu Indonesia (MLI) / PT. Jenaka Sumber Rezeki, especially regarding the Business Model Canvas (BMC) analysis. The business model is one method that helps companies tell stories about a company's internal conditions. Through BMC, companies can display the nine essential components of a business in a simple but comprehensive way. The research data was obtained from the results of a literature study in the form of books, news articles, journals, YouTube video content, researchers’ experiences, and other supporting literature sources. The results obtained from this study were that the MLI company optimally implemented the nine components of BMC and benefited from various business sources such as talent management fees, YouTube Adsense, selling merchandise, holding special online and offline shows, MLI Music, MLI e-sports, and endorsements. Keyword: Business Model Canvas, Comedy Canvas, Majelis Lucu Indonesi

An Analysis of Business Model Canvas on a Comedy Company (A Study Case of Majelis Lucu Indonesia/PT. Jenaka Sumber Rezeki)

This study aims to determine the business model of a talent development company or comedy company at Majelis Lucu Indonesia (MLI) / PT. Jenaka Sumber Rezeki, especially regarding the Business Model Canvas (BMC) analysis. The business model is one method that helps companies tell stories about a company's internal conditions. Through BMC, companies can display the nine essential components of a business in a simple but comprehensive way. The research data was obtained from the results of a literature study in the form of books, news articles, journals, YouTube video content, researchers’ experiences, and other supporting literature sources. The results obtained from this study were that the MLI company optimally implemented the nine components of BMC and benefited from various business sources such as talent management fees, YouTube Adsense, selling merchandise, holding special online and offline shows, MLI Music, MLI e-sports, and endorsements. Keyword: Business Model Canvas, Comedy Canvas, Majelis Lucu Indonesi

Leveraging Contextual Cues for Generating Basketball Highlights

The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in. The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies. Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games.Comment: Proceedings of ACM Multimedia 201

The role of motion analysis in elite soccer

The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game especially due to the increased physical demands of match-play. The monitoring of players’ work-rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university- based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, identify practical implications of the established body of knowledge, while highlighting areas that require further exploration

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

A framework for event detection in field-sports video broadcasts based on SVM generated audio-visual feature model. Case-study: soccer video

In this paper we propose a novel audio-visual feature-based framework, for event detection in field sports broadcast video. The system is evaluated via a case-study involving MPEG encoded soccer video. Specifically, the evidence gathered by various feature detectors is combined by means of a learning algorithm (a support vector machine), which infers the occurrence of an event, based on a model generated during a training phase, utilizing a corpus of 25 hours of content. The system is evaluated using 25 hours of separate test content. Following an evaluation of results obtained, it is shown for this case, that both high precision and recall statistics are achievable

Automatic parsing of sports videos with grammars

Motivated by the analogies between languages and sports videos, we introduce a novel approach for video parsing with grammars. It utilizes compiler techniques for integrating both semantic annotation and syntactic analysis to generate a semantic index of events and a table of content for a given sports video. The video sequence is first segmented and annotated by event detection with domain knowledge. A grammar-based parser is then used to identify the structure of the video content. Meanwhile, facilities for error handling are introduced which are particularly useful when the results of automatic parsing need to be adjusted. As a case study, we have developed a system for video parsing in the particular domain of TV diving programs. Experimental results indicate the proposed approach is effectiv

The THUMOS Challenge on Action Recognition for Videos "in the Wild"

Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task. In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos. These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions. The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world. In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures. We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection. We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches. Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos. We conclude by proposing several directions and improvements for future THUMOS challenges.Comment: Preprint submitted to Computer Vision and Image Understandin