Application of AdaBoost algorithm in basketball player detection

Abstract

Video materials contain huge amount of information. Their storage in databases and analysis by various algorithms is a constantly developing area. This paper presents the process of basketball game analysis by AdaBoost algorithm. This algorithm is mainly used for face and body parts recognition, and was not tested on player detection in basketball. It consists of a linear combination of weak classifiers. In this paper, we used stumps, i.e. decision trees with only one level as such classifiers. The aim of this research is to assess the accuracy of this algorithm when applied in player detection during basketball games. We examined the capabilities of AdaBoost algorithm on a video footage obtained from the single moving camera, without any previous processing. First training was performed using images of a basketball player’s entire body (head, legs, arms and torso), while the second training was performed using images of a head and torso. By applying the algorithm to the given set of images that include head and torso, the algorithm obtained an accuracy of 70.5%. Training on the set of entire body images was not successful due to the large amount of background that goes into the training, and which represents noise in training process. This research concluded that AdaBoost could not be applied to object detection in sports events. We also concluded that this algorithm gives much better results when applied on simpler objects (like face recognition) and that its application could be in detection of players’ body parts or as a first step in object detection in order to eliminate as much area as possible. Its application in detecting players' upper body or entire players gives large number of false positive, which makes algorithm inapplicable in real situations.