Abstract
People reidentification is one of the most challenging tasks in computer vision, and considerable efforts have been directed toward providing solutions to this problem. The existence of extensive camera networks and surveillance systems increases the amount of people images obtained, but, on the other hand, implies the need for new algorithms to enable reidentification of people captured by the cameras. There is no one optimal model that solves the entire problem, but a set of distinctive features can be used to help in the matching process. Our proposal consists of using the orientation of each person captured in the surveillance scene to considerably improve the reidentification process. An iterative algorithm maximizes the number of successful matches and speeds up the process. A comparison with other earlier relevant studies is presented using available datasets.
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