61,084 research outputs found
Towards robots reasoning about group behavior of museum visitors: leader detection and group tracking
The final publication is available at IOS Press through http://dx.doi.org/10.3233/AIS-170467Peer ReviewedPostprint (author's final draft
WiFi emission-based vs passive radar localization of human targets
In this paper two approaches are considered for human targets localization based on the WiFi signals: the device emission-based localization and the passive radar. Localization performance and characteristics of the two localization techniques are analyzed and compared, aiming at their joint exploitation inside sensor fusion systems. The former combines the Angle of Arrival (AoA) and the Time Difference of Arrival (TDoA) measures of the device transmissions to achieve the target position, while the latter exploits the AoA and the bistatic range measures of the target echoes. The results obtained on experimental data show that the WiFi emission-based strategy is always effective for the positioning of human targets holding a WiFi device, but it has a poor localization accuracy and the number of measured positions largely depends on the device activity. In contrast, the passive radar is only effective for moving targets and has limited spatial resolution but it provides better accuracy performance, thanks to the possibility to integrate a higher number of received signals. These results also demonstrate a significant complementarity of these techniques, through a suitable experimental test, which opens the way to the development of appropriate sensor fusion techniques
Inferring models of bacterial dynamics toward point sources
Experiments have shown that bacteria can be sensitive to small variations in
chemoattractant (CA) concentrations. Motivated by these findings, our focus
here is on a regime rarely studied in experiments: bacteria tracking point CA
sources (such as food patches or even prey). In tracking point sources, the CA
detected by bacteria may show very large spatiotemporal fluctuations which vary
with distance from the source. We present a general statistical model to
describe how bacteria locate point sources of food on the basis of stochastic
event detection, rather than CA gradient information. We show how all model
parameters can be directly inferred from single cell tracking data even in the
limit of high detection noise. Once parameterized, our model recapitulates
bacterial behavior around point sources such as the "volcano effect". In
addition, while the search by bacteria for point sources such as prey may
appear random, our model identifies key statistical signatures of a targeted
search for a point source given any arbitrary source configuration
Evolutionary Robot Vision for People Tracking Based on Local Clustering
This paper discusses the role of evolutionary computation in visual perception for partner robots. The search of evolutionary computation has many analogies with human visual search. First of all, we discuss the analogies between the evolutionary search and human visual search. Next, we propose the concept of evolutionary robot vision, and a human tracking method based on the evolutionary robot vision. Finally, we show experimental results of the human tracking to discuss the effectiveness of our proposed method
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