Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.
This paper addresses the problem of tracking basketball players in broadcast videos. This is a challenging scenario where multiple occlusions are present, the resolution of the players is small and there is a high similarity between the different instances to track, specially within the same team members. For a deeper review of players detection and tracking in sports the interested reader is referred to the recent survey @cite_14 . The authors of @cite_15 also consider basketball scenarios seen from a broadcast camera and they deal with player identification. For that, they propose to use CNN features extracted at multiple scales and encoded in a Fisher vector.
@cite_15 @cite_14
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This paper addresses the problem of automatic player identification in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this setting is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires sophisticated approaches to capture distinctive features from players to distinguish them. To this end, we use Convolutional Neural Networks (CNN) features extracted at multiple scales and encode them with an advanced pooling, called Fisher vector. We leverage it for exploring representations that have sufficient discriminatory power and ability to magnify subtle differences. We also analyze the distinguishing parts of the players and present a part based pooling approach to use these distinctive feature points. The resulting player representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96 on NBA basketball clips. Tracking and identifying players in sports videos filmed with a single pan-tilt-zoom camera has many applications, but it is also a challenging problem. This paper introduces a system that tackles this difficult task. The system possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players. The identification system combines three weak visual cues, and exploits both temporal and mutual exclusion constraints in a Conditional Random Field (CRF). In addition, we propose a novel Linear Programming (LP) Relaxation algorithm for predicting the best player identification in a video clip. In order to reduce the number of labeled training data required to learn the identification system, we make use of weakly supervised learning with the assistance of play-by-play texts. Experiments show promising results in tracking, homography estimation, and identification. Moreover, weakly supervised learning with play-by-play texts greatly reduces the number of labeled training examples required. The identification system can achieve similar accuracies by using merely 200 labels in weakly supervised learning, while a strongly supervised approach needs a least 20,000 labels. Abstract Vision-based player detection and classification are important in sports applications. Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as intelligent broadcasts and event classification. In this paper, we present a convolutional neural network (CNN) that satisfies all these requirements. The network contains a three-branch proposal network and a four-cascade classification network. Our method first trains these cascaded networks from labeled image patches. Then, we efficiently apply the network to a whole image by using a dilation strategy in testing. We conducted experiments on soccer, basketball, ice hockey and pedestrian datasets. Experimental results demonstrate that our method can accurately detect players under challenging conditions. Compared with CNNs that are adapted from general object detection networks such as Faster-RCNN, our approach achieves state-of-the-art accuracy on three types of games (basketball, soccer and ice hockey) with 1000 × fewer parameters. The generality of our method is also demonstrated on a standard pedestrian detection dataset in which our method achieves competitive performance compared with state-of-the-art methods. This paper proposes the automatic annotation of sports video for content-based retrieval. Conventional methods using position information of objects such as locus, relative positions, their transitions, etc., as indices, have drawbacks that tracking errors of a certain object due to occlusions causes recognition failures, and that representation by position information essentially has a limited number of recognizable events in the retrieval. Our approach incorporates human behavior analysis and specific domain knowledge with conventional methods, to develop an integrated reasoning module for richer expressiveness of events and robust recognition. Based on the proposed method, we implemented a content-based retrieval system which can identify several actions on real tennis video. We select court and net lines, players' positions, ball positions, and players' actions, as indices. Court and net lines are extracted using a court model and Hough transforms. Players and ball positions are tracked by adaptive template matching and particular predictions against sudden changes of motion direction. Players' actions are analyzed by 2D appearance-based matching using the transition of players' silhouettes and a hidden Markov model. The results using two sets of tennis video is presented, demonstrating the performance and the validity of our approach.