Basketball Sport Action Recognitionbased on Spatio-Temporal Interest Points

• Main Authors: Jia-Hsing Li, 李佳星
• Other Authors: Shu-Yuan Chen
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/79403618555642828467

碩士 === 元智大學 === 資訊工程學系 === 101 === Identification of human behavior in video clips is an important issue in the field of computer vision, which can be widely used in video retrieval, security surveillance, human-computer interaction, remote care and so on. This study presents a classification algorithm based on Spatio-Temporal Interest Points (STIP) to identify basketball behavior in video clips. First, the STIPs are detected in basketball video and their characteristics are described in terms of the direction of the gradient, Histograms of Oriented Gradient (HOG), and optical flow, Histogram of Optical Flow (HOF). Matching scores are then calculated using constraints on matching pairs and consistency. More specifically, the match (or correspondence) of a STIP point in the test video is defined as the nearest neighbor (NN) of all the STIP points from the model video based on the Euclidean distance of the STIP descriptor vectors in terms of HOG and HOF. A matching is regarded valid only if the matching distance is less a threshold. Matching score is then computed according to the number of valid matching pairs and the degree of consistency among valid matching. The matching scores are used to classify basketball behavior. The proposed method uses publicly library Action Similarity LAbeliNg (ASLAN) and selects video clips of basketball from the database to prove the feasibility of our method. Experimental results confirmed that the proposed method is promising.