Towards Cooperative People Re-Identification between 3D Sensors and 2D Camera Networks

Thanks to the increasing popularity of 3D sensors, robotics vision has experienced huge improvements in a wide range of applications and systems in the last years. Besides the many benets, this migration caused some incompatibilities with those systems that cannot be based on range sensors, like intelligent video surveillance systems, since the two kinds of sensor data lead to dierent representations of people and objects. This work goes in the direction of bridging the gap, and presents a novel re-identication system that takes advantage of multiple video flows in order to enhance the performance of a skeletal tracking algorithm, which is in turn exploited for driving the re-identication. A new, geometry-based method for joining together the detections provided by the skeletal tracker from multiple video flows is introduced: it is capable of dealing with many people in the scene, and of rejecting the errors introduced in each view by the skeletal tracker. Such method has a high degree of generality, and it can be applied to any body pose estimation algorithm. The system was tested on a public dataset for video surveillance applications, demonstrating the improvements achieved by the multi-viewpoint approach in the accuracy of both the body pose estimation and the re-identication. This means that the lack of the rich information provided by 3D sensors can be compensated by the availability of more than one viewpoint.