Tracking of moving athlete from video sequences using flower pollination algorithm

Abstract

Performance analysis, as related to sport, is a process underpinned by a systematic analysis of information, to accelerate the performance of athletes through crafted focused practice session based on the obtained analysis. Quantification of athlete performance profile using sports video has thus been put forward, where the athlete tracking in such video-based analysis is one of the critical elements for the success of an object tracking system. In this study, for the first time the flower pollination algorithm (FPA) is utilised to track the motion of the moving athlete from the sports video. Initially, a search window with the attributes of centroid coordinates of the moving athlete, width and length of the search window is used to represent the current position of the athlete. Subsequently, the hue, saturation and value (HSV) histogram of the region within the search window is evaluated. In the consecutive frame, several potential positions of the athlete are identified, and the Bhattacharyya distance between the HSV histogram of the athlete in the previous frame and the potential position in the current frame is calculated. Since the FPA attempts to maximise the similarity of both histograms, intuitively, the current position of the moving athlete should be only slightly different than his previous position. The comparative analysis shows that the FPA is comparable with other competing algorithms in terms of detection rate, tracking accuracy and processing time.