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Recently in Dufrenois [1], a new Fisher type contrast measure has been proposed to extract a target population in a dataset contaminated by outliers. Although mathematically sound, this work presents some further shortcomings in both the formalism and the field of use. First, we propose to re-express this problem from the formalism of proximal support vector machines as introduced in Mangasarian and...
This paper presents a method for multi-target tracking in a multisensor system composed of several distributed active sensors (rangefinders). The final goal is to deliver a complete reconstruction of the environment of a vehicle. The originality of this work relies on the joint exploitation of geometric invariance properties of objects avoiding any loss of optimality and on an efficient management...
This paper presents a new method of multiple object tracking in a multisensor lidar context. One of the difficulties of scanning laser range finders (LRF) lies in the measurements of the objects since they are spatially distributed. We propose a tracking method (MF-IP-SMC), which uses the LRF data from a clustering stage (IP method) which avoids the usual detection step. This step is indeed source...
This paper presents a detection and tracking approach of multiple vehicles in scanning laser range data. The proposed solution relies on a new detection method based on object geometric invariant that uses the raw measurements directly in polar coordinates. The multitarget management problem is solved in the PHD framework by a particle filter.
In the field of road safety, a key problem concerns the advanced driver assistance systems (ADAS). One of the objectives is to define systems that monitor the vehicle's environment and inform the driver about the surrounding vehicles. The final idea is to warn him about potentially hazardous situations. In this context, the scanning laser rangefinder is a very popular sensor. It has the advantage...
This paper presents a method for joint detection and tracking of vehicles in scanning laser range data. Many methods use a solution that processes the raw data in a detection procedure and then tracks the detected object in an association/tracking procedure. The proposed approach uses a preclustering stage (SIP) as an input of the tracking process that allows to manage the displacement of the center-of-gravity...
In this paper, we propose a kernel hat matrix based learning stage for outlier removal. In particular, we show that the Gaussian kernel hat matrix have very interesting discriminative properties under the condition of choosing appropriate values for kernel parameters. Thus, we develop a practical model selection criteria in order to well separate the ldquooutlierrdquo distribution from the ldquodominantrdquo...
The authors propose a high-level scenario recognition algorithm for video sequence interpretation. The recognition of scenarios is based on a Bayesian networks approach. The model of a scenario contains two main layers. The first one allows events from the observed visual features to be highlighted and the second layer is focused on the temporal reasoning stage. The temporal layer uses specific nodes...
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