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The problem is joint detection and tracking of possibly several objects moving through a region of interest. A wireless sensor network (WSN), deployed in the region, collects the acoustic energy measurements and sends them to the fusion center for processing. The problem is cast in the sequential Bayesian estimation framework and solved using a particle filter. The number of objects is unknown and...
Tracking objects using multiple sensors is more efficient than those using one sensor. In this paper, we proposed a method to fuse data from multiple sensors in Gaussian mixture probability hypothesis density filter. This method can avoid the data association problem in multi-sensor multi-object tracking. Moreover, it is more reliable and less computational than particle probability hypothesis density...
In this paper we will consider several algorithms for tracking closely spaced objects. In particular we will concentrate on various particle filter implementations. One particular problem when using a joint multi target particle filter is the so-called mixed labelling problem. This problem amounts to the fact that different particles will have a different labelling w.r.t. target identity. The combination...
This paper presents our work which involves the application of a recursive Bayesian filter, the Gaussian mixture probability hypothesis density (GMPHD) filter, to a visual tracking problem. Foreground objects are detected using statistical background modeling to obtain measurements which are input into the filter. The GMPHD filter explicitly models the birth, survival and death of objects by managing...
The fusion of data from different sensorial sources is today the most promising method to increase robustness and reliability of environmental perception. The paper presents an approach for using fuzzy operators for the hierarchical fusion of processing results in a multi sensor data processing system for the detection of vehicles in road environments. Tracking and fusion of intermediate results is...
Multi-sensor systems in automotive safety applications and sensor data fusion have become very popular in recent years. Sensors on board cars and active safety applications are increasing in number and the need to define a common method for object extraction and serving these applications has been recognized. Authors propose a high level fusion approach suitable for automotive sensor networks with...
The paper presents a spatial-color model of object and develops an efficient visual tracking algorithm based on particle filter. This spatial-color model captures richer information than the general color histogram because it incorporates spatial distribution of pixels in addition to color. In order to fast compute the weight of each particle, the Integral Images for computation of histogram, mean...
In uncontrolled environments, with dynamic background and lighting changes, performing efficient and real-time foreground - background segmentation is very challenging. This work is based on the hypothesis that the combination of long wave infrared (LWIR) (8-12 mum) and colour cameras can significantly improve the robustness of moving objects extraction. Pros and cons of colour and thermal imagers...
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