Abstract:
Smart surveillance in smart cities has become an important feature to be used in
resource utilization and city-wide security areas. Multi-target multi-camera tracking
has been one of the core areas in smart surveillance since the overlapped field of
views within cameras cannot be expected in the real world scenarios. The
inefficiency in MTMCT has caused this feature to not be used in real time
applications. Hence how to make vehicle re-identification feature signature matching
efficient in multi target multi camera tracking has become a research problem.
This research introduces a trajectory based probabilistic search algorithm to reduce
target search space and increase the efficiency of the MTMCT. The solution consists
of a YOLO v4 based object detection module, IOU based single camera tracking
module, OSNet based feature extraction module and a cross camera identification
module using probabilistic target search algorithm. The system takes video streams
in and outputs the global trajectory of each target target. The evaluation is done using
identification F1 score and the efficiency was measured using the number of frames
processed in a second.
Citation:
Wijayasekara, K.S. (2022). Multi-Target multi-camera tracking optimization using probabilistic target search [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21480