Abstract:
The real-time based object recognition is very important and challenging tasks in computer
vision, as there is an increasing interest in building an intelligent system. A smart tour guide
system for the museum enables an exhibition more attractive by improving the interaction
between the visitors and the interest of objects by means of a guide. The National Museum of
Ethiopia is still present its exhibits a rather passive and non-engaging way. The prior works were
used a NVIDIA shielded Tablet that has NVIDIA GPU to run the developed the context-based
object recognizer that could create an interactive museum exhibition. The proposed model,
mobile-based smart tour guide, can understand the context of the visit and visitor behavior and
thereby filtering out which the visitor is interested in. A YOLOv3-tiny model was trained on the
Darknet framework and the .weightsfile was converted to an intermediate weight (TensorFlow)
called .pb file. And then, .pb file was converted to its TensorFlow Lite equivalent, .tflite file
which enables on-device object detection with low latency and small binary size. The context
of the visits and visitor’s behavior is analyzed by applying video processing pipelines on an
online (real-time) video data and thereby the recognizer is stabled and hence it recognizes an
interested object. First, the problem of multiple detections of the same object is cured by Non maximum Suppression (NMS). Second, those objects far from a visitor are ignored from being
recognized. Third, the prediction of object persists for a C number of frames only is taken as
interested object and is done by greedy data association tracking-by-detection techniques.
Finally, the interested object id is filtered out and the object within that id is displayed on the mobile screen for further description about it to the visitor. So, the developed deep learning
model able to detect objects with better accuracy by 97.47% and 69% on object detection and
recognition respectively which averages an overall accuracy of 83.24%
