Technology

Sentio’s cutting edge soccer player tracking technology is specifically designed to accurately track players in real time by utilizing the state-of-the-art computer vision and machine learning algorithms.

After 3 years of academic effort, Sentio Research has developed and released its core application, the Sentioscope®, that processes images captured from a 4K camera setup on a laptop to detect and track multiple soccer players in real-time.

Figure 1
Figure 1

Combined color and motion likelihood of players being on conceptual soccer field cells.

Figure 2
Figure 2

Tracking players in occlusion by softly clustering conceptual soccer field cells.

“Accurately tracking soccer players is a non-trivial task due to existence of various challenges. Soccer players try to confuse each other with unexpected changes in their velocity. Moreover players almost look identical and they are frequently involved in possession challenges and tackles in which they could be completely occluded by another resulting in tracking ambiguities.”


Sentio’s player tracking algorithm is specifically designed to address the problems above and precisely track players in rain or shine. “In our approach, the soccer field is conceptually modeled as a two dimensional world and partitioned into a grid consisting of dense spatial cells. Each cell corresponds to unique ground point on the soccer field and is represented by a fixed image patch in the video.”


We employ a well-known machine learning technique and a classifier, trained with over 100k player samples, to detect the set of cells containing players in the video. This allows our tracking algorithm to function in a variety of challenging environmental conditions.


Our probabilistic algorithm tracks the players by calculating their likelihood of being on the cells using a combined motion and color model as shown in Figure 1. The likelihoods of the players are then evaluated globally in order to capture the interactions of the players and track them accurately even in full occlusions as seen on Figure 2.