Human tracking and activity recognition are receiving increasing attention among computer scientists due to the wide spectrum of applications where they can be used, ranging from athletic performance analysis to video surveillance. By human tracking, we refer to the ability of a computer to recover the position and orientation of the limbs of a human from a sequence of images. There have been several different approaches to allow computers to derive automatically the kinematic. In recent years, there has been an increasing interest in monocular human tracking and activity recognition systems, due to a large number of applications where those features can be used. Standard algorithms are not practical to employ for human tracking due to the computational cost that arises from the high number of degrees of freedom of the human body and from the ambiguity of the images obtained from a single camera. Constraints in the configuration of the human body can be used to reduce its complexity. The constraints can be deduced from the demonstration, based on the human performance of different activities. A human tracking system is developed using this kind of constraints and then evaluated. The fact that the constraints are based on activities allows while doing the tracking, the inference of the activity the human is performing.

Reference Paper: Kinematic Tracking and Activity Recognition Using Motion Primitives
Author’s Name: German Gonzalez
Source: Royal Institute of Technology

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