This paper considers the statistics of local appearance based measures that are suitable for the visual parsing of sport events. The moments of the colour information are computed, and the shape content in the frames is characterised by the moments of local shape measures. Their generation process is very low cost. The temporal evolution of the features then is modelled with a Hidden Markov Model. The HMM is used to generate higher level information by classifying the shots as close ups, court views, crowd shots and so on. The paper illustrates how those simple features, coupled with the HMM, can be used for parsing snooker and tennis footages.
Temporal and spatial random variation of luminance in images, or
'flicker' is a typical degradation observed in archived film and
video. The underlying premise in typical flicker reduction algorithms is that each image must be corrected for a spatially varying gain and
offset. These parameters are estimated in the stationary region of the
image. Hence the performance of that algorithm depends crucially on the identification of stationary image regions. Position fluctuations are also a common artefact resulting in a random 'shake' of each film frame. For removing both, the key is to reject regions showing local motion or other outlier activity. Parameters are then estimated mostly on that part of the image undergoing the dominant motion. A new
algorithm that simultaneously deals with global motion estimation and
flicker is presented. The final process is based on a robust application of weighted least-squares, in which the weights also classify portions of the image as local or global. The paper presents results on severely degraded sequences showing evidence of both Flicker and random shake.