Sports-player CV tracking with motion models.
A method and device generates a trajectory. The method includes receiving a plurality of tracklets indicative of movement of a plurality of targets over a predetermined temporal interval. The method includes determining a plurality of context data for a pair of tracklets based upon at least one additional tracklet. The method includes computing a probability that the pair of tracklets relate to a first one of the targets. The method includes generating a trajectory for the first target based upon a concatenation of select ones of the tracklets. The concatenation maximizes the probability that the pair of tracklets correspond to the first target based upon the context data associated with the pair of the tracklets.
BACKGROUND INFORMATION(1) A model of probabilistic object motion is required to track objects as they move so that a tracking algorithm may determine a trajectory from a set of hypotheses that is the most realistic. If multiple objects are being tracked concurrently, prior data is used to evaluate the feasibility of a set of simultaneous trajectories for all the objects being tracked. Conventionally, most multi-object tracking algorithms utilize a drastic simplification to keep the inference problem tractable. Specifically, each of the objects is evaluated in isolation such that each object is tracked independently of other objects. However, object motions are not always independent. In team sports, for example, player movements are highly correlated to both nearby and distant players. It is highly inaccurate to evaluate each trajectory using the conventional method of isolating each object. However, it is also quite difficult to determine and optimize a complex model which describes all possible interactions between players.(2) With regard to multi-target tracking, this has been a difficult problem of broad interest in the technical field of computer vision. Surveillance is a common scenario in which multi-target tracking is utilized. Team sports are another popular domain utilizing multi-target tracking that has a wide range of applications in strategy analysis, automated broadcasting, and content-based retrieval. Recent developments in pedestrian tracking have utilized a f
1. A method, comprising: receiving a plurality of tracklets indicative of movement of a plurality of targets over a predetermined temporal interval; determining a plurality of context data for a pair of tracklets based upon at least one additional tracklet, wherein the context data is indicative of a situation in a team sports match; computing a probability that the pair of tracklets relate to a first one of the targets, wherein the probability is determined based upon a regression; generating a trajectory for the first target based upon a concatenation of select ones of the tracklets, wherein the concatenation maximizes the probability that the pair of tracklets correspond to the first target based upon the context data associated with the pair of the tracklets, wherein the trajectory is used to control an orientation of a measurement device capturing at least part of the movement of the plurality of targets over the predetermined temporal interval, wherein the orientation corresponds to further movement of the first target of the plurality of targets over a further predetermined time interval.
9. A device, comprising: a processor coupled to a memory, wherein the processor is programmed to generate a trajectory for a first target of a plurality of targets by: receiving a plurality of tracklets indicative of movement of the targets over a predetermined temporal interval; determining a plurality of context data for a pair of tracklets based upon at least one additional tracklet, wherein the context data is indicative of a situation in a team sports match; computing a probability that the pair of tracklets relate to a first one of the targets, wherein the probability is determined based upon a regression; generating a trajectory for the first target based upon a concatenation of select ones of the tracklets, wherein the concatenation maximizes the probability that the pair of tracklets correspond to the first target based upon the context data associated with the pair of the tracklets, wherein the trajectory is used to control an orientation of a measurement device capturing at least part of the movement of the plurality of targets over the predetermined temporal interval, wherein the orientation corresponds to further movement of the first target of the plurality of targets over a further predetermined time interval.
16. A non-transitory computer readable storage medium with an executable program stored thereon, wherein the program instructs a microprocessor to perform operations comprising: receiving a plurality of tracklets indicative of movement of a plurality of targets over a predetermined temporal interval; determining a plurality of context data for a pair of tracklets based upon at least one additional tracklet, wherein the context data is indicative of a situation in a team sports match; computing a probability that the pair of tracklets relate to a first one of the targets, wherein the probability is determined based upon a regression; generating a trajectory for the first target based upon a concatenation of select ones of the tracklets, wherein the concatenation maximizes the probability that the pair of tracklets correspond to the first target based upon the context data associated with the pair of the tracklets, wherein the trajectory is used to control an orientation of a measurement device capturing at least part of the movement of the plurality of targets over the predetermined temporal interval, wherein the orientation corresponds to further movement of the first target of the plurality of targets over a further predetermined time interval.