Methods and systems of spatiotemporal pattern recognition for video content development
An enhanced video of an event in a first video feed, which is identified by a spatiotemporal pattern recognition algorithm that uses machine learning for understanding the event, is produced by including in the enhanced video an animation that characterizes a person's motions that are derived from a machine learning-based understanding of an event in a second video.
1. A method for delivering personalized video content, comprising:
processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of at least one event within the at least one video feed of a professional game, wherein the understanding developed by the machine learning includes an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed;
capturing 3D video of a non-professional player as a data feed;
developing an understanding using the machine learning of at least one event within the data feed relating to motion of the non-professional player; and
automatically, under computer control, providing an enhanced video feed that mixes video of the non-professional player with the at least one video feed of the professional game and represents the nonprofessional player as an animation having attributes based on the data feed relating to motion of the non-professional player playing within a context of the professional game based on the understanding of the at least one event within the at least one video feed of the professional game and the data feed relating to the motion of the non-professional player.
2. The method of claim 1 , wherein using the machine learning to develop the understanding of the at least one event further comprises using the at least one event in position tracking data over time obtained from at least one of the at least one video feed and a chip-based player tracking system, and wherein the understanding is based on at least two of spatial configuration, relative motion, and projected motion of at least one of a player and an item used in a game.
3. The method of claim 1 , wherein using the machine learning to develop the understanding of the at least one event further comprises aligning multiple unsynchronized input feeds related to the at least one event using at least one of a hierarchy of algorithms and a hierarchy of human operators, wherein the unsynchronized input feeds are selected from a group consisting of one or more broadcast video feeds of the event, one or more feeds of tracking video for the event, and one or more play-by-play data feeds of the at least one event.
4. The method of claim 3 , wherein the multiple unsynchronized input feeds include at least three feeds selected from at least two types related to the event.
5. The method of claim 3 , further comprising at least one of validating and modifying the alignment of the unsynchronized input feeds using a hierarchy involving at least two of one or more algorithms, one or more human operators, and one or more input feeds.
6. The method of claim 1 , further comprising at least one of validating the understanding and modifying the understanding using a hierarchy involving at least two of one or more algorithms, one or more human operators, and one or more input feeds.