Improved generalized labeled multiBernoulli filter for nonellipsoidal extended targets or group targets tracking based on random submatrices Abstract   For nonellipsoidal extended targets and group targets tracking, using an ellipsoid to approximate the target extension may not be accurate enough because of lacking shape and orientation information. In consideration of this, we model a nonellipsoidal extended target or target group as a combination of multiple ellipsoidal subobjects, each represented by a random matrix. Besides, in practical tracking scenes, the knowledge of detection probability is of critical importance, which, however, is prior unknown due to the fluctuation of target parameters. Thus based on the aforementioned random submatrices model, an improved generalized labeled multiBernoulli (GLMB) filter is presented for the tracking of nonellipsoidal extended targets or group targets in the case of unknown detection probability. In addition, a cheaper Gibbs sampling based joint prediction and updating implementation of the proposed GLMB filter is given, within which the extended targets or group targets are modeled using Beta gamma Gaussian inverse Wishart (BGGIW) distributions, achieving the estimation to detection probability, measurement rate, and extension state of each subobject and the kinematic state with the positions of all subobjects and unified kinematics for each extended target or target group. The simulation results demonstrate the effectiveness of the proposed filter.

OnLine Visual Tracking with Occlusion Handling One of the core challenges in visual multitarget tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of deja vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled MultiBernoulli (GLMB) filter, originally designed for tracking pointsized targets, to be used in visual multitarget tracking. The proposed algorithm includes a novel false alarm detectionremoval and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the stateoftheart methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the stateoftheart methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.