2 research outputs found
A Unified Framework for Shot Type Classification Based on Subject Centric Lens
Shots are key narrative elements of various videos, e.g. movies, TV series,
and user-generated videos that are thriving over the Internet. The types of
shots greatly influence how the underlying ideas, emotions, and messages are
expressed. The technique to analyze shot types is important to the
understanding of videos, which has seen increasing demand in real-world
applications in this era. Classifying shot type is challenging due to the
additional information required beyond the video content, such as the spatial
composition of a frame and camera movement. To address these issues, we propose
a learning framework Subject Guidance Network (SGNet) for shot type
recognition. SGNet separates the subject and background of a shot into two
streams, serving as separate guidance maps for scale and movement type
classification respectively. To facilitate shot type analysis and model
evaluations, we build a large-scale dataset MovieShots, which contains 46K
shots from 7K movie trailers with annotations of their scale and movement
types. Experiments show that our framework is able to recognize these two
attributes of shot accurately, outperforming all the previous methods.Comment: ECCV2020. Project page: https://anyirao.com/projects/ShotType.htm
A Survey on Content-Aware Video Analysis for Sports
Sports data analysis is becoming increasingly large-scale, diversified, and
shared, but difficulty persists in rapidly accessing the most crucial
information. Previous surveys have focused on the methodologies of sports video
analysis from the spatiotemporal viewpoint instead of a content-based
viewpoint, and few of these studies have considered semantics. This study
develops a deeper interpretation of content-aware sports video analysis by
examining the insight offered by research into the structure of content under
different scenarios. On the basis of this insight, we provide an overview of
the themes particularly relevant to the research on content-aware systems for
broadcast sports. Specifically, we focus 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. Content-aware analysis methods are discussed with respect to
object-, event-, and context-oriented groups. In each group, the gap between
sensation and content excitement must be bridged using proper strategies. In
this regard, a content-aware approach is required to determine user demands.
Finally, the paper summarizes the future trends and challenges for sports video
analysis. We believe that our findings can advance the field of research on
content-aware video analysis for broadcast sports.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT