Earticle

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

A novel field line detection method is proposed based on the directional information of field line. First, the special character of modern soccer playfield is considered, and a novel soccer field model is introduced which includes offside assistant lines. By designing a directional filter, these field lines can be accurately detected. Moreover, the field line detection and tracking for a clip is developed, which can further improve the performance of field line detection. In this paper, we integrate the basic theories of multimedia analysis, pattern recognition and computer vision into content analysis for sports video analysis. Through exploring several components such as object detection, camera calibration and tactic analysis, a systematical system is built up. The proposed algorithms can be not only used for soccer video analysis but also used for other sports video analysis. It provides a promising method for general multimedia analysis.