Tracking objects using multiple cues yields more robust results. The well-known hidden Markov model (HMM) provides a powerful framework to incorporate multiple cues by expanding its observation. However, a plain HMM does not capture the inter-correlation between measurements of neighboring states when computing the transition probabilities. This can seriously damage the tracking performance. To...

Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception system. The requirements of a environment system cannot be satisfied by either the existing automotive sensors. A commonly used sensor cluster for these consists mono-vision smart camera radar. fusion is intended to combine data sensors perform robust perception. Multi-object trackin...

In this paper we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake, which causes additional detections. When the detections are fed to a tracking system like the probabilistic data association filter (PDAF), the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even ...

In prior work, we developed a speaker tracking system based on an extended Kalman filter using time delays of arrival (TDOAs) as acoustic features. In particular, the TDOAs comprised the observation associated with an iterated extended Kalman filter (IEKF) whose state corresponds to the speaker position. In other work, we followed the same approach to develop a system that could use both audio ...

Multiple target tracking (MTT) is taken into account as one of the most important topics in tracking targets with radars. In this paper, the MTT problem is used for estimating the position of multiple targets when a 2-D radar is employed to gather measurements. To do so, the Joint Probabilistic Data Association Filter (JPDAF) approach is applied to tracking the position of multiple targets. To ...

It has been shown that the dynamic environment around the mobile robot can be efficiently and robustly represented by the Bayesian occupancy filter (BOF) [1]. In the BOF framework, this environment is decomposed into a gridbased representation in which both the occupancy and the velocity distributions are estimated for each grid cell. In a such representation, concepts such as objects or tracks...

It has been shown that the dynamic environment around the mobile robot can be efficiently and robustly represented by the Bayesian occupancy filter (BOF) [1]. In the BOF framework, the environment is decomposed into a gridbased representation in which both the occupancy and the velocity distributions are estimated for each grid cell. In such a representation, concepts such as objects or tracks ...

The problem of limited sensor resolution, although usually ignored in target tracking, occurs in multi-target scenarios whenever the target distance falls below the size of the sensor resolution cell. Typical examples are the surveillance of aircraft in formation, and convoy tracking for ground surveillance. Ignoring the limited sensor resolution in a tracking system may lead to degraded tracki...

Recognising behaviours of multiple people, especially high-level behaviours, is an important task in surveillance systems. When the reliable assignment of people to the set of observations is unavailable, this task becomes complicated. To solve this task, we present an approach, in which the hierarchical hidden Markov model (HHMM) is used for modeling the behaviour of each person and the joint ...