Master thesis Robust Multi-Hypothesis Tracker Fusing Diverse Sensor Information

This Master’s Thesis presents a developed tracking algorithm which fuses different object detections. These object detections are obtained from different detectors. These detectors are: a laser leg detector and a tag vision detector. The fusion has the objective to obtain more robust object detections; moreover, it has the capability to distinguish specific people. In the future, it is intend to add new detectors, such as: a skeleton person detector and a radar detector. Adding new detectors is direct and easy, since the tracker has a general and flexible implementation which allows the change of the type and number of detectors. The Robust multi-hypothesis tracker fusing diverse sensor information uses as the backbone of the theory the multi-hypothesis tracker approach proposed by Reid [28]. Then, the algorithm has been modified to add some new improvements. For work with social robots in the algorithm was added two functions in the probabilities of the hypothesis which allows us to control the confirmation and the deletion of the tracks. Also, the algorithm was improved by adding another function that lets we use the people’s velocities orientations to improve the association between tracks and detections in crossing situations. Furthermore, the algorithm was extended with the fusion of different detections. For work with autonomous vehicles in the European Project Cargo ANTs the algorithm was adapted for tracking locally (without map) and global (with a map); was improved to use the track velocity and a specific detector to distinguish between dynamic and static objects and was adapted to group different objects of the same type using distance. The objects can have dynamic or static type. Finally, the tests of the tracker algorithm have been carried out in the fields of social robots and autonomous vehicles (Cargo ANTs project). The experiments have been carried out with Tibi and Dabo robots, illustrate that our tracking approach can robustly and efficiently track multiple people without changes on their id ’s and without losing them. Also, the experiments show that the tracker can distinguish a specific person by using the fusion; even in situations which has several people or objects that cross with or occlude the specific person. The experiments have been carried out in the Cargo ANTs project show that the tracker is able to follow only the single real dynamic object, and that the tracker is able to group different detections obtained inside the same object.