@mastersthesis {435, title = {Tracking Multiple Objects with Kalman Filters, part II}, year = {2006}, month = {02/2006}, pages = {68}, publisher = {Department of Electronics}, type = {Master}, address = {Alcala de Henares (SPAIN)}, abstract = {

In this report the implementation of a multiple object tracking algorithm is described. The algorithm is part of the obstacle avoidance system in an autonomous robot. The measurement vector used to achieve the tracking task comes from a stereo-vision system that detects objects in the robot\’s environment [1]. The algorithm uses the probabilistic Kalman filter (KF) to estimate the position and movement of different objects in the scene. One filter is used for each object to track. An algorithm for associating the data in the measurement vector to different objects is described. A validation process that the tracking algorithm uses to reduce the noise included in the measurement vector is also described.

}, keywords = {Erasmus, Kalman filters, Multi-Object Tracking, Real-time implementation, TFC}, attachments = {https://geintra-uah.org/en/system/files/Thesis_-_Micke.pdf}, author = {Mikael Lindeborg}, editor = {Marta Marron} }