PROCEEDINGS IPMU '08
Detection and Recognition confidences update in a multi-sensor pedestrian tracking system
Fadi FAYAD, Véronique CHERFAOUI.
We present in this paper a method for
confidence updating in a multi-sensor
pedestrian tracking system. We focus
on the computing of detection and
recognition confidence indicators
according to the object detection and/or
recognition probabilities provided by
sensor modules. We propose to use the
Transferable Belief Model in order to
model and combine the sensor module
outputs at different times. Since
detection and recognition processes are
not really independent, we propose to
use the cautious rule to combine the
belief functions. We propose a track
confidences updating algorithm and its
interesting behavior is shown on
synthetic data.
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