Highlights in a sport video are usually referred as actions that stimulate excitement or attract attention of the audience. A big effort is spent in designing techniques which find automatically highlights, in order to automatize the otherwise manual editing process. Most of the state-of-the-art approaches try to solve the problem by training a classifier using the information extracted on the tv-like framing of players playing on the game pitch, learning to detect game actions which are labeled by human observers according to their perception of highlight. Obviously, this is a long and expensive work. In this paper, we reverse the paradigm: instead of looking at the gameplay, inferring what could be exciting for the audience, we directly analyze the audience behavior, which we assume is triggered by events happening during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to extract visual features from cropped video recordings of the supporters that are attending the event. Outputs of the crops belonging to the same frame are then accumulated to produce a value indicating the Highlight Likelihood (HL) which is then used to discriminate between positive (i.e. when a highlight occurs) and negative samples (i.e. standard play or time-outs). Experimental results on a public dataset of ice-hockey matches demonstrate the effectiveness of our method and promote further research in this new exciting direction.
@cite_16 , Suksai and Ratanaworabhan propose an approach that combines on-line information retrieval with text extraction using OCR techniques. This way, they are able to limit the number of false positives.
@cite_16
2591089465
Quantity and quality of sport highlights attract viewers to sport programs and portal sites. Making a good quality highlight requires an experience production team. It is a job that is very hard to automate because of the quality factor. The machines of the current generation are not intelligent enough to make a sound judgment on what establishes a highlight yet. This work focuses on this difficult problem. It introduces a new technique to automate the making of sport highlight that is of acceptable quality. Previous approaches to extracting the highlights employ image and or audio processing technology to capture relevant contexts related to highlight moments, e.g., goal frame to hint at the scoring moment. However, the accuracy of such approaches has not been satisfactory. They generate many false positives that may render the highlights useless. To remedy this, we propose a new approach that combines on-line information retrieval with text extraction using optical character recognition (OCR) - a much more tractable problem than general image processing. The new approach makes better highlights because it is free of false positives by design.