Paper Info
Title
Vertical Axis Detection For Sport Video Analytics
Abstract
When processing video, it is normally assumed that cameras are vertically oriented such that people appear upright, which helps simplify subsequent processing such as person detection. In real situations, due to the need to provide maximum coverage of the viewing space, cameras are usually placed with arbitrary orientations so the apparent vertical axis of the videos captured may not correspond to the true vertical direction of the captured scene. To rectify this situation, we propose a classification-based system, which normalizes the video compensating for the camera orientation. We demonstrate the performance of the system for outdoor sports video. Our system works as follows: From an arbitrary set of sports videos, we first automatically create a training/testing image dataset, in which players have various orientations. Our classifier is a stacked autoencoder connected to a softmax output layer, which is trained using this dataset for estimating the orientation of players. The orientation of an input video is normalized according to the orientations of player patches, whose angles of orientation are estimated by the above trained classifier. The experiments conducted on hockey field video dataset show that the proposed system is able to estimate the true vertical axis of an input video accurately.
Year
Venue
Keywords
2016
2016 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA)
sparse autoencoder, video orientation normalisation, human detection
Field
DocType
Citations 
Computer vision,Autoencoder,Normalization (statistics),Softmax function,Pattern recognition,Vertical direction,Computer science,Video tracking,Artificial intelligence,Classifier (linguistics),Analytics,Calibration
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
rui zeng1214.18
Ruan Lakemond200.34
Simon Denman350956.72
Sridha Sridharan42092222.69
Clinton Fookes574397.41
Stuart Morgan6958.96