Broadcast Baseball Videos Research Articles

An efficient pitch-by-pitch extraction algorithm through multimodal information

Sport video analysis facilitates the discovery of semantic structures in sport broadcast videos and enables a wide spectrum of applications. For example, coaches can analyze offensive and defensive plays performed during games to assess a team’s capabilities. In general, identifying interested shots, e.g. pitch shots, from broadcast baseball videos requires great human labor to browse through those videos. In this work, we proposed a novel technique that automatically extracts pitch-by-pitch shots by recognizing the reliable emergence of pitching speed displayed on the scoreboard, estimating when and where the pitcher is present, and identifying the pitch shots based on the pitcher’s motion degree. To validate the performance and accuracy of the proposed technique, we collected a dataset of baseball videos broadcasted in various countries. The experimental results verify that the proposed technique successfully extracts the desired pitch-by-pitch videos. Furthermore, it outperforms the state-of-the-art approach in terms of accuracy and time complexity.

HMM-based ball hitting event exploration system for broadcast baseball video

With the dramatic growth of fandom population, a considerable amount of research efforts have been devoted to baseball video processing. However, little work focuses on the detailed follow-ups of ball hitting events. This paper proposes a HMM-based ball hitting event exploration system for broadcast baseball video. Utilizing the strictly-defined layout of the baseball field, the proposed system first detects the game-specific spatial patterns in the field, such as the field lines, the bases, the pitch mound, etc. Then, the play region—the currently camera-focused region of the baseball field is identified for frame type classification. Since the temporal patterns of presenting the game progress follow a prototypical order, we consider the classified frame types as observation symbols and recognize ball hitting events using HMM. Experiments conducted on broadcast baseball video show encouraging results in frame type classification and ball hitting event recognition. Three practical applications, including highlight clip extraction by user-designated query, storyboard construction, and similar event retrieval, are introduced to address the applicability of our system.

Automatic Pitch Type Recognition System from Single-View Video Sequences of Baseball Broadcast Videos

This article describes a system that automatically recognizes individual pitch types like screwballs and sliders in baseball broadcast videos. These decisions are currently made by human specialists in baseball, who are watching the broadcast video of the game. No automatic system has yet been developed for identifying individual pitch types from single view camera images. Techniques using multiple fixed cameras promise highly accurate pitch type identification, but the systems tend to be large. Our system is designed to identify the same pitch types using only the same single-view broadcast baseball videos used by the human specialists, and accordingly we used a number of features, such as the ball’s location, ball speed and catcher’s stance based on the advice of those specialists. The system identifies the pitch type using a classifier trained with the Random Forests ensemble learning algorithm and achieved about 90% recognition accuracy in experiments.

Contour-based strike zone shaping and visualization in broadcast baseball video: providing reference for pitch location positioning and strike/ball judgment

The strike zone plays a crucial role in baseball. Pitches which pass through the strike zone count as strikes, three of which strike out the batter. Besides strike/ball judgment, the strike zone also provides the reference for positioning pitch locations, about which sports fans and professionals have an intense interest in compiling statistics. This paper presents contour-based strike zone shaping and visualization in broadcast baseball video. We first detect the home plate, which determines the vertical limits of the strike zone. Then, an efficient algorithm is designed for batter contouring and the points of curvature extremes on the batter's contour are computed to locate the dominant points which determine the horizontal limits of the strike zone. The experiments show that the proposed framework is able to shape the strike zone fairly well in various baseball sequences, needless of manual operation and additional camera setting. The proposed strike zone shaping and visualization will be able to assist in pitch analysis and statistics compiling.

Event Detection of Broadcast Baseball Videos

This paper presents an effective and efficient event detection system for broadcast baseball videos. It integrates midlevel cues including scoreboard information and shot transition patterns into event classification rules. First, a simple scoreboard detection and recognition scheme is developed to extract the game status from videos. Then, a shot transition classifier is designed to obtain the shot transition patterns, which contains several novel schemes including adaptive playfield segmentation, pitch shot detection, field shot detection, as well as infield/outfield classification. The extracted midlevel cues are used to develop an event classifier based on a Bayesian Belief Network. The network is with low complexity because the number of these cues used is small, which not only improves the generalization performance of the event classifier but also reduces system complexity as well as training efforts. Using the inference results of the network, we further derive a set of classification rules to identify baseball events. The set of rules is stored in a look-up table such that the classification is only a simple table look-up operation. The proposed approach is very simple and computational efficient. More importantly, the simulation results indicate that it identifies ten significant baseball events with 95% of precision rate and 92% of recall rate, which is very promising.

A Trajectory-Based Ball Tracking Framework with Visual Enrichment for Broadcast Baseball Videos *

Pitching contents play the key role in the resultant victory or defeat in a baseball game. Utilizing the physical characteristic of ball motion, this paper presents a trajectory-based framework for automatic ball tracking and pitching evaluation in broadcast baseball videos. The task of ball detection and tracking in broadcast baseball videos is very challenging because in video frames, the noises may cause many ball-like objects, the ball size is small, and the ball may deform due to its high speed movement. To overcome these challenges, we first define a set of filters to prune most non-ball objects but retain the ball, even if it is deformed. In ball position prediction and trajectory extraction, we analyze the 2D distribution of ball candidates and exploit the characteristic that the ball trajectory presents in a near parabolic curve in video frames. Most of the non-qualified trajectories are pruned, which greatly improves the computational efficiency. The missed balls can also be recovered in the trajectory by applying the position prediction. The experiments of ball tracking on the testing sequences of JPB, MLB and CPBL captured from different TV channels show promising results. The ball tracking framework is able to extract the ball trajectory, superimposed on the video, and in near real-time provide visual enrichment before the next pitch coming up without specific cameras or equipments set up in the stadiums. It can also be utilized in strategy analysis and intelligence statistics for player training.