Abstract:
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ABSTRACT
Intelligent city and traffic management depend on city-scale multi-camera vehicle
tracking; however, this work has various difficulties. On the different viewing angles,
problems arise that include the variation on the large scale, frequent occlusion and
appearance variation. In this study, we use the cross-camera tracking technique and
multi-camera tracking system that considers aggregation loss. To address the challenges
of multi-camera vehicle tracking, the suggested system has four key parts. First, we
extract the tracks with the help of a single camera view by identifying the object and
multi-object tracking modules. These modules combine their detection capabilities to
provide efficient tracking between frames. After obtaining the tracklets, we use a multi-
camera re-identification module to match them.
