Automatic player detection and identification for sports entertainment applications

Zahid Mahmood*, Tauseef Ali, Shahid Khattak, Laiq Hasan, Samee U. Khan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

29 Citations (Scopus)

Abstract

In this paper, we develop an augmented reality sports broadcasting application for automatic detection, recognition of players during play, followed by display of personal information of players. The proposed application can be divided into four major steps. In first step, each player in the image is detected. In the second step, a face detection algorithm detects face of each player. In third step, we use a face recognition algorithm to match the faces of players with a database of players’ faces which also stores personal information of each player. In step four, personal information of each player is retrieved based on the face matching result. This application can be used to show the viewers’ information about players such as name of the player, sports record, age, highest score, and country of belonging. We develop this system for baseball game, however, it can be deployed in any sports where the audience can take a live video or images using smart phones. For the task of player and subsequent face detection, we use AdaBoost algorithm with haar-like features for both feature selection and classification while player face recognition system uses AdaBoost algorithm with linear discriminant analysis for feature selection and nearest neighbor classifier for classification. Detailed experiments are performed using 412 diverse images taken using a digital camera during baseball match. These images contain players in different sizes, facial expressions, lighting conditions and pose. The player and face detection accuracy is high in all situations, however, the face recognition module requires detected players’ faces to be frontal or near frontal. In general, restricting the head rotation to ±30 gives a high accuracy of overall system.
Original languageEnglish
Pages (from-to)971-982
Number of pages12
JournalPattern analysis and applications
Volume18
Issue number4
DOIs
Publication statusPublished - Sep 2014

Keywords

  • SCS-Safety
  • LDA
  • NNC
  • Face detection and recognition
  • Adaboost

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