Text Driven Temporal Segmentation of Cricket Videos

Sankar, K. Pramod; Pandey, Saurabh; Jawahar, C. V.

doi:10.1007/11949619_39

Cited by 13 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They use an AlexNet with an SVM for classification. Sankar et al [19] and Sharma et al [23] have focused on the shot segmentation problem in Cricket videos using text alignment.…”

Section: Literature Surveymentioning

confidence: 99%

Cricket Stroke Recognition Using Hard and Soft Assignment Based Bag of Visual Words

Gupta

Karel

Muthiah

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

In this work, we deal with the problem of activity recognition in Cricket telecast videos. We present a supervised approach for recognizing Cricket stroke categories using two variants of Bag of Visual words (BoV) model applied on dense optical flow based motion feature i.e., grid-based flattened vectors and orientation histograms, 3D ResNet extracted features and 2D ResNet extracted spatial features. These globally extracted features, in spite of the noise due to camera motion, give good results on the Cricket strokes dataset having 562 trimmed stroke videos. We independently labeled the strokes based on the direction of stroke play and the direction of camera motion, into five and three categories. respectively, and provide experimental analysis on Hard Assignment (HA) and Soft Assignment (SA) based BoV methods.Our contribution lies in showing the effectiveness of the unordered BoV representation for direction based action recognition. The experimental analysis presented in our work provides an insight into the BoV HA and SA models applied to global frame-level feature descriptors. Moreover, our experiments suggest the orientation histograms to be simple yet effective for direction based recognition tasks. The best accuracy achieved on the Cricket strokes test partition was 85.85% with orientation histograms and 82.08% with grid features, for 3 category and 5 category labels, respectively.

show abstract

“…They use an AlexNet with an SVM for classification. Sankar et al [19] and Sharma et al [23] have focused on the shot segmentation problem in Cricket videos using text alignment.…”

Section: Literature Surveymentioning

confidence: 99%

Cricket Stroke Recognition Using Hard and Soft Assignment Based Bag of Visual Words

Gupta

Karel

Muthiah

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…KP Sankar et al [5] have proposed a text driven temporal segmentation. They use annotated text data from a website and align the video to it.…”

Section: Introductionmentioning

confidence: 99%

Automatic summarization of broadcast cricket videos

Kumar

Gupta

Ravì

et al. 2011

2011 IEEE 15th International Symposium on Consumer Electronics (ISCE)

View full text Add to dashboard Cite

Summarization of cricket videos is very important because of three reasons: 1) its long duration making manual highlights generation tedious 2) less explored area compared to other sports like soccer 3) huge viewership. We propose a novel summarization scheme for cricket which exploits its contextual semantics. First, we detect the bowling frames based on which the video is temporally segmented into individual deliveries. Then each temporal segment representing a delivery is classified into an interesting or non-interesting segment based on detection of events namely boundaries and wickets. Due to the high frequency of ads and replays in cricket, we have proposed robust algorithms for their removal. Finally, we have proposed a finite state automaton based modeling of the temporal segments to extract key-frames. We have also extended the framework to include text cues and expert choices and also developed a hierarchical summary. We have tested our algorithm on several broadcast cricket videos and obtained good results.

show abstract