Maheshkumar H. Kolekar

Bayesian belief network based broadcast sports video indexing

This paper presents a probabilistic Bayesian belief network (BBN) method for automatic indexing of excitement clips of sports video sequences. The excitement clips from sports video sequences are extracted using audio features. The excitement clips are comprised of multiple subclips corresponding to the events such as replay, field-view, close-ups of players, close-ups of referees/umpires, spectators, players’ gathering. The events are detected and classified using a hierarchical classification scheme. The BBN based on observed events is used to assign semantic concept-labels to the excitement clips, such as goals, saves, and card in soccer video, wicket and hit in cricket video sequences. The BBN based indexing results are compared with our previously proposed event-association based approach and found BBN is better than the event-association based approach. The proposed scheme provides a generalizable method for linking low-level video features with high-level semantic concepts. The generic nature of the proposed approach in the sports domain is validated by demonstrating successful indexing of soccer and cricket video excitement clips. The proposed scheme offers a general approach to the automatic tagging of large scale multimedia content with rich semantics. The collection of labeled excitement clips provide a video summary for highlight browsing, video skimming, indexing and retrieval.

Event-Importance Based Customized and Automatic Cricket Highlight Generation

In this paper, we present a novel approach towards customized and automated generation of sports highlights from its extracted events and semantic concepts. A recorded sports video is first divided into slots, based on the game progress and for each slot, an importance-based concept and event-selection is proposed to include those in the highlights. Using our approach, we have successfully extracted highlights from recorded video of cricket match

Stator winding fault prediction of induction motors using multiscale entropy and grey fuzzy optimization methods

The prediction of stator winding faults using multiscale entropy is performed for the first time.Real-time vibration and current are used as diagnostics to identify faults.The system complexity associated with motors is investigated using multiscale entropy.GFRG is used to predict fault and also to suggest optimal settings for motor operation.The motor condition has a maximum contribution of 54.21%, as determined from the ANOVA analysis. In the present work, stator winding fault prediction is studied using a multiscale entropy (MSE) algorithm combined with a grey-based fuzzy algorithm. Experiments were performed with a normal motor and a motor with faulty stator winding. Real time, motor current and vibration signals were acquired at different operating speeds and were used for the diagnosis of faults. The obtained signals were denoised by wavelet transform. Grey relational analysis (GRA) coupled with fuzzy logic was used to model the stator winding fault and to predict the optimal setting for running the induction motor within its parameters range. Analysis of variance (ANOVA) was performed to determine the effect of each individual parameter on the response. The results indicate that the proposed novel approach is very effective in predicting the stator winding fault. Furthermore, the best running parameters for the induction motor are also reported.

Bayesian Network-Based Customized Highlight Generation for Broadcast Soccer Videos

Sports highlight generation techniques aim at condensing a full-length video to a significantly shortened version that still preserves the main interesting content of the original video. In this paper, we present the system for automatically generating the highlights from sports TV broadcasts. The proposed system detects exciting clips based on audio features and then classify the individual scenes within the clip into events such as replay, player, referee, spectator, and players gathering. A probabilistic Bayesian belief network based on observed events is used to assign semantic concept-labels to the exciting clips, such as goals, saves, yellow-cards, red-cards, and kicks in soccer video sequences. The labeled clips are selected according to their degree of importance to include in the highlights. We have successfully generated highlights from soccer video sequences.

A hierarchical framework for generic sports video classification

A five layered, event driven hierarchical framework for generic sports video classification has been proposed in this paper. The top layer classifications are based on a few popular audio and video content analysis techniques like short-time energy and Zero Crossing Rate (ZCR) for audio and Hidden Markov Model (HMM) based techniques for video, using color and motion as features. The lower layer classifications are done by applying game specific rules to recognize major events of the game. The proposed framework has been successfully tested with cricket and football video sequences. The event-related classifications bring us a step closer to the ultimate goal of semantic classifications that would be ideally required for sports highlight generation.

Semantic Event Detection and Classification in Cricket Video Sequence

In this paper, we present a novel hierarchical framework and effective algorithms for cricket event detection and classification. The proposed scheme performs a topdown video event detection and classification using hierarchical tree which avoids shot detection and clustering. In the hierarchy, at level-1, we use audio features, to extract excitement clips from the cricket video. At level-2, we classify excitement clips into real-time and replay segments. At level-3, we classify these segments into field view and non-field view based on dominant grass color ratio. At level-4a, we classify field view into pitch-view, long-view, and boundary view using motion-mask. At level-4b, we classify non-field view into close-up and crowd using edge density feature. At level-5a, we classify close-ups into the three frequently occurring classes batsman, bowler/fielder, umpire using jersey color feature. At level-5b, we classify crowd segment into the two frequently occurring classes spectator and playerspsila gathering using color feature. We show promising results, with correctly classified cricket events, enabling structural and temporal analysis, such as highlight extraction, and video skimming.

Hidden Markov model based video indexing with discrete cosine transform as a likelihood function

In this paper, we have proposed discrete cosine transform (DCT) as a likelihood function for video shot detection and classification using hidden Markov model and experimentally observed that only DC coefficient of DCT performs better with heavily reducing computational burden. Results of DC-DCT likelihood function are presented and found encouraging.

Semantic concept mining in cricket videos for automated highlight generation

This paper presents a novel approach towards automated highlight generation of broadcast sports video sequences from its extracted events and semantic concepts. A sports video is hierarchically divided into temporal partitions namely, megaslots, slots, and semantic entities, namely concepts, and events. The proposed method extracts event sequence from video and classifies each sequence into a concept by sequential association mining. The extracted concepts and events within the concepts are selected according to their degree of importance to include those in the highlights. A parameter degree of abstraction is proposed, which gives a choice to the user about how concisely the extracted concepts should be produced for a specified highlight duration. We have successfully extracted highlights from recorded video of cricket match and compared our results with the manually-generated highlights by sports television channel.

Experimental Investigation of Misalignment Effects on Rotor Shaft Vibration and on Stator Current Signature

Misalignment is one of the most common faults in any rotating machine. It can cause decrease in efficiency and in the long run may cause failure of machine. Most of the researchers, consider only vibration information for the misalignment. However, this paper inspects the different types of misalignments by using diagnostic medium such as stator current signature as well as rotor vibration signal and it is being found that current signature alone can predict the misalignment effect without use of vibration signal. Diagnostic features obtained by FFT related to misalignments have been explained. Orbit plots are effectively used to explain the unique nature of misalignment fault. In this study, shaft displacement and stator current samples during machine run up under aligned as well as misaligned conditions are measured and analyzed. Result shows that misalignment is the parameter that is more responsible for the cause of instability.

A hierarchical framework for semantic scene classification in soccer sports video

In this paper, we propose a novel hierarchical framework for soccer (football) video classification. Unlike most existing video classification approaches, which focus on shot detection followed by classification based on clustering using shot aggregation, the proposed scheme perform a top-down video scene classification which avoids shot clustering. This improves the classification accuracy and also maintains the temporal order of shots. In the hierarchy, at level-1, we use audio features, to extract potentially interesting clips from the video. At level-2, we classify these clips into field view and non-field view using feature of dominant grass color ratio. At level-3a, we classify field view into three kinds of views using motion-mask. At level-3b, we classify non-field view into close-up and crowd using skin color information. At level-4, we classify close-ups into the four frequently occuring classes such as player of team-A, player of team-B, goalkeeper of team-A, goalkeeper of team-B using jersey color information. We show promising results, with correctly classified soccer scenes, enabling structural and temporal analysis, such as highlight extraction, and video skimming.