Discriminative Label Propagation for Multi-Object Tracking with Sporadic Appearance Features

Given a set of plausible detections, detected at each time instant independently, we investigate how to associate them across time. This is done by propagating labels on a set of graphs that capture how the spatio-temporal and the appearance cues promote the assignment of identical or distinct labels to a pair of nodes. The graph construction is driven by the locally linear embedding (LLE) of either the spatio-temporal or the appearance features associated to the detections. Interestingly, the neighborhood of a node in each appearance graph is defined to include all nodes for which the appearance feature is available (except the ones that coexist at the same time). This allows to connect the nodes that share the same appearance even if they are temporally distant, which gives our framework the uncommon ability to exploit the appearance features that are available only sporadically along the sequence of detections. Once the graphs have been defined, the multi-object tracking is formulated as the problem of finding a label assignment that is consistent with the constraints captured by each of the graphs. This results into a difference of convex program that can be efficiently solved. Experiments are performed on a basketball and several well-known pedestrian datasets in order to validate the effectiveness of the proposed solution

Tracking system of known objects in unstable lightning conditions on stage using IR and visible spectrum video inputs

In this research work we address the multiple object tracking problem under challenging illumination conditions. To solve the changing illumination problem, we implement two well known tracking approaches, which are operating in two separated domains. One tracker relies on active marker system, which operates in near infrared spectrum and the other one exploits existing state of the art pedestrian detector and color information by observing the scenery in visible spectrum. By integrating these two approaches, we intend to improve the performance of object tracking in closed areas, rather than using only one of the introduced trackers. This also means that the implemented system is usable in many practical applications, as it is designed to track up to 16 moving objects. In the first part of the thesis, we cover existing research work, which was done in this field, then we describe implementation and evaluation details of the system and finally we propose a set of directions for future research and possible improvements of our system