Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking

Multi-Sensor Real-Time Data Fusion on Embedded Computing Platforms

Continuous technological progress facilitates the development of embedded vehicle classification systems. These systems typically have a lower availability of resources than on customary personal computers. They consist of several components such as sensor data acquisition and preprocessing, feature extraction and feature-/decision-based data fusion. Besides visual, Lidarand Radar-based informa...

Multi - Sensor Data Fusion for Representing and Tracking Dynamic Objects

Multi-Sensor Management for Data Fusion in Target Tracking

Multi-sensor management for data fusion in target tracking concerns issues of sensor assignment and scheduling by managing or coordinating the use of multiple sensor resources. Since a centralized sensor management technique has a crucial limitation in that the failure of the central node would cause whole system failure, a decentralized sensor management (DSM) scheme is increasingly important ...

Real-Time Video Processing for Multi-Object Chromatic Tracking

This paper presents MOCT, a multi-object chromatic tracking technique for real-time natural video processing. Its main step is the MOCT localization algorithm, that performs local data evaluations in order to apply a multiple output parallel reduction operator to the image. The reduction operator is used to localize the positions of the object centroids, to compute the number of pixels occupied...

Statistical Information Fusion for Multiple-View Sensor Data in Multi-Object Tracking

This paper presents a novel statistical information fusion method to integrate multiple-view sensor data in multi-object tracking applications. The proposed method overcomes the drawbacks of the commonly used Generalized Covariance Intersection method, which considers constant weights allocated for sensors. Our method is based on enhancing the Generalized Covariance Intersection with adaptive w...