Improved target tracking with road network information

Abstract

Abstract-In this paper we consider the problem of tracking targets, which can move both on-road and off-road, with particle filters utilizing the road-network information. It is argued that the constraints like speed-limits and/or oneway roads generally incorporated into on-road motion models make it necessary to consider additional high-bandwidth off-road motion models. This is true even if the targets under consideration are only allowed to move on-road due to the possibility of imperfect road-map information and drivers violating the traffic rules. The particle filters currently used struggles during sharp mode transitions, with poor estimation quality as a result. This is due to the fact the number of particles allocated to each motion mode is varying according to the mode probabilities. A recently proposed interacting multiple model (IMM) particle filtering algorithm, which keeps the number of particles in each mode constant irrespective of the mode probabilities, is applied to this problem and its performance is compared to a previously existing algorithm. The results of the simulations on a challenging bearing-only tracking scenario show that the proposed algorithm, unlike the previously existing algorithm, can achieve good performance even under the sharpest mode transitions.

Pedestrian tracking with an infrared sensor using road network information

“…In Section 5.2 the comparison between MMPF and a standard off-road tracker is made on a similar data set with a GPS trajectory as the ground truth. A Monte-Carlo (MC) study based on synthetic data is presented in Section 5.3 where the IMM-PF [37,7] is also evaluated in order to come to a judgement about the expected differences between different multiple model particle filters. Finally, in Section 5.4 an example illustrating the use and the performance gain of negative information is shown.…”

“…In general, all of the different multiple model particle filters are expected to give similar performance results for our application, which is also confirmed by the comparison between MMPF and IMM-PF of [7] we present in Section 5.3. Nevertheless, it must still be noted that there might be pathological examples (see e. g., [37]) for which these algorithms would yield significantly different performances especially during mode transitions.…”

“…In the particle filter this means that the weight i is updated according to (37) where a = 1. When the possibly occluded regions in the scene are known, this information can be used as a form of negative information in the particle filter at time instants with no detection.…”

“…The main objective in this section is to show the advantage of using a multiple model particle filter with road network knowledge over a standard PF. In addition to MMPF another multiple model particle filter, the IMMPF [7,37], is also evaluated. IMMPF is similar to MMPF, but the number of particles is constant and predefined for each mode, unlike the MMPF where the number of particles in each mode is varying according to the posterior mode probabilities.…”

“…An example using the VS-MMPF methodology is presented in [36] where user selected number of particles can be used in each mode of the filter by making use of the so-called "variable-mass" idea. Another important alternative is the interacting multiple model particle filter (IMM-PF) of [7] which is applied to the road target tracking case in [37] with an on-road and off-road mode.…”