text: We present a parameterized approach to produce personalized variable length summaries of soccer matches. Our approach is based on temporally segmenting the soccer video into 'plays', associating a user-specifiable 'utility' for each type of play and using 'bin-packing' to select a subset of the plays that add up to the desired length while maximizing the overall utility (volume in bin-packing terms). Our approach systematically allows a user to override the default weights assigned to each type of play with individual preferences and thus see a highly personalized variable length summarization of soccer matches. We demonstrate our approach based on the output of an end-to-end pipeline that we are building to produce such summaries. Though aspects of the overall end-to-end pipeline are human assisted at present, the results clearly show that the proposed approach is capable of producing semantically meaningful and compelling summaries. Besides the obvious use of producing summaries of superior league matches for news broadcasts, we anticipate our work to promote greater awareness of the local matches and junior leagues by producing consumable summaries of them.
  @cite_19: Soccer highlight detection is an active research topic in recent years. One of the difficult problems is how to effectively fuse multi-modality cues, i.e. audio, visual and textual information, to improve the detection performance. This paper proposes a novel two-dependence Bayesian network (2d-BN) based fusion approach to soccer highlight detection. 2d-BN is a particular Bayesian network which assumes that each variable depends on two other variables at most. Through this assumption, 2d-BN can not only characterize the relationships among features but also be trained efficiently. Extensive experiments demonstrate the effectiveness of the proposed method.
  @cite_21: 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.
  @cite_1: 
  @cite_6: Tracking the ball is critical for video-based analysis of team sports. However, it is difficult, especially in low-resolution images, due to the small size of the ball, its speed that creates motion blur, and its often being occluded by players. In this paper, we propose a generic and principled approach to modeling the interaction between the ball and the players while also imposing appropriate physical constraints on the ball's trajectory. We show that our approach, formulated in terms of a Mixed Integer Program, is more robust and more accurate than several state-of-the-art approaches on real-life volleyball, basketball, and soccer sequences.
summary: Sports video summarization in general has seen considerable traction in recent times -- player and ball motion tracking @cite_6 , highlight generation using audio visual cues @cite_19 @cite_1 etc. are being actively pursued in constrained settings. A recent and an interesting approach to generate basketball highlights @cite_21 leverages multimodal context cues for video summarization. While the system asserts good results in indoor basketball settings, replicating same to the outdoor settings would be a challenge in the absence of high quality broadcast data complemented with player and play-by-play stats. Local soccer matches or junior leagues are devoid of such elaborate setups and we only have video data in such settings. We thus propose a generic parameterized approach based on visual motion' cues to generate highlights of the soccer matches.