Association-free Tracking of Two Closely Spaced Targets

This paper introduces a new concept for tracking closely spaced targets in Cartesian space based on position measurements corrupted with additive Gaussian noise. The basic idea is to select a special state representation that eliminates the target identity and avoids the explicit evaluation of association probabilities. One major advantage of this approach is that the resulting likelihood function for this special problem is unimodal. Hence, it is especially suitable for closely spaced targets. The resulting estimation problem can be tackled with a standard nonlinear estimator. In this work, we focus on two targets in two-dimensional Cartesian space. The Cartesian coordinates of the targets are represented by means of extreme values, i.e., minima and maxima. Simulation results demonstrate the feasibility of the new approach

Vehicle infrastructure cooperative localization using Factor Graphs

Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicle-infrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approac

The Thermal Infrared Visual Object Tracking VOT-TIR2015 challenge results

The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Link - ping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015

Optimal Gaussian Filtering for Polynomial Systems Applied to Association-free Multi-Target Tracking

This paper is about tracking multiple targets with the so-called Symmetric Measurement Equation (SME) filter. The SME filter uses symmetric functions, e.g., symmetric polynomials, in order to remove the data association uncertainty from the measurement equation. By this means, the data association problem is converted to a nonlinear state estimation problem. In this work, an efficient optimal Gaussian filter based on analytic moment calculation for discrete-time multi-dimensional polynomial systems corrupted with Gaussian noise is derived, and then applied to the polynomial system resulting from the SME filter. The performance of the new method is compared to an UKF implementation by means of typical multiple target tracking scenarios

The visual object tracking VOT2015 challenge results

The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website

Cooperative vehicle-infrastructure localization based on the symmetric measurement equation filt

Precise and accurate localization is important for safe autonomous driving. Given a traffic scenario which has multiple vehicles equipped with internal sensors for self-localization, and external sensors from the infrastructure for vehicle localization, vehicle-infrastructure communication can be used to improve the accuracy and precision of localization. However, as the number of vehicles in a scenario increases, associating measurement data with the correct source becomes increasingly challenging. We propose a solution utilizing the symmetric measurement equation filter (SME) for cooperative localization to address data association issue, as it does not require an enumeration of measurement-to-target associations. The principal idea is to define a symmetrical transformation which maps measurements to a homogeneous function, thereby effectively addressing several challenges in vehicle-infrastructure scenarios such as data association, bandwidth limitations and registration/configuration of the external sensor. To the best of our knowledge, the proposed solution is among the first to address all these issues of cooperative localization simultaneously, by utilizing the topology information of the vehicles