Abstract:
Automated visual analysis of the object is of prime
importance to realize the real-time concept of the internet of
things. In this paper, we proposed a real-time fine grained visual
analytics system for tracing the visibility of products on retail
store shelves. The proposed visual monitoring system (VMS) is
aimed to achieve high rates of product recognition, regardless of
several real-time challenges like occlusion, different lightening
conditions, product orientation etc. To address all these issues,
the VMS collects the local feature descriptors which are scale
invariant, rotational invariant and illumination invariant from
training template images. Once, the testing image uploaded
from any camera enabled device, the VMS extracts same local
features and matches with the target feature descriptors for finegrained
object recognition. This paper also covers the
performance of various state-of-the-art local feature descriptors
for object detection in context of retail store
monitoring/tracking. The performance of the VMS is tested on
real time retail shelve images. The results after investigation, the
proposed fine-grained VMS shows approximately 90%
accuracy in brand level detection.
