This paper presents a method to assess a basketball player's performance from his her first-person video. A key challenge lies in the fact that the evaluation metric is highly subjective and specific to a particular evaluator. We leverage the first-person camera to address this challenge. The spatiotemporal visual semantics provided by a first-person view allows us to reason about the camera wearer's actions while he she is participating in an unscripted basketball game. Our method takes a player's first-person video and provides a player's performance measure that is specific to an evaluator's preference. To achieve this goal, we first use a convolutional LSTM network to detect atomic basketball events from first-person videos. Our network's ability to zoom-in to the salient regions addresses the issue of a severe camera wearer's head movement in first-person videos. The detected atomic events are then passed through the Gaussian mixtures to construct a highly non-linear visual spatiotemporal basketball assessment feature. Finally, we use this feature to learn a basketball assessment model from pairs of labeled first-person basketball videos, for which a basketball expert indicates, which of the two players is better. We demonstrate that despite not knowing the basketball evaluator's criterion, our model learns to accurately assess the players in real-world games. Furthermore, our model can also discover basketball events that contribute positively and negatively to a player's performance.
These kinematic data are often obtained by multiple third-person videos @cite_16 @cite_31 @cite_11 where the players and ball are detected using recognition algorithms combined with multiple view geometry @cite_37 . Tracking and data association is a key issue where the role of the players provides a strong cue to disambiguate appearance based tracking @cite_23 . Events such as ball movement, can be also recognized using a spatiotemporal analysis @cite_19 . As players behave strategically and collectively, their group movement can be predicted @cite_21 and the ball can be localized without detection. Various computational models have been used for such tasks, e.g., Dynamic Bayesian Network @cite_4 , hierarchical LSTM @cite_5 , attention based LSTM @cite_25 learned from a large collection of third-person videos.
@cite_37 @cite_4 @cite_21 @cite_19 @cite_23 @cite_5 @cite_31 @cite_16 @cite_25 @cite_11
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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. In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabling temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data. In this paper, we investigate a weakly-supervised object detection framework. Most existing frameworks focus on using static images to learn object detectors. However, these detectors often fail to generalize to videos because of the existing domain shift. Therefore, we investigate learning these detectors directly from boring videos of daily activities. Instead of using bounding boxes, we explore the use of action descriptions as supervision since they are relatively easy to gather. A common issue, however, is that objects of interest that are not involved in human actions are often absent in global action descriptions known as "missing label". To tackle this problem, we propose a novel temporal dynamic graph Long Short-Term Memory network (TD-Graph LSTM). TD-Graph LSTM enables global temporal reasoning by constructing a dynamic graph that is based on temporal correlations of object proposals and spans the entire video. The missing label issue for each individual frame can thus be significantly alleviated by transferring knowledge across correlated objects proposals in the whole video. Extensive evaluations on a large-scale daily-life action dataset (i.e., Charades) demonstrates the superiority of our proposed method. We also release object bounding-box annotations for more than 5,000 frames in Charades. We believe this annotated data can also benefit other research on video-based object recognition in the future. In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by examining individual person actions and their relations. Temporal dynamics exist both at the level of individual person actions as well as at the level of group activity. Given a video sequence as input, methods can be developed to capture these dynamics at both person-level and group-level detail. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. In order to model both person-level and group-level dynamics, we present a 2-stage deep temporal model for the group activity recognition problem. In our approach, one LSTM model is designed to represent action dynamics of individual people in a video sequence and another LSTM model is designed to aggregate person-level information for group activity recognition. We collected a new dataset consisting of volleyball videos labeled with individual and group activities in order to evaluate our method. Experimental results on this new Volleyball Dataset and the standard benchmark Collective Activity Dataset demonstrate the efficacy of the proposed models.