Abstract
Existing works on surveillance robots in indoor scenarios focus only on specific event detections and fail to detect most indoor objects due to lack of proper training. The present disclosure receives and pre-processes stream of input images specific to plurality of scenes related to indoor space from robot mounted camera. Pre-processed stream of input images is passed to a first trained model to obtain plurality of bounding boxes and masks pertaining to objects. An object property detector algorithm is run on masks to detect properties of objects. Classify signages comprised in masks as text signage by passing through optical character recognition or symbol by passing through second trained model. Convert output of optical character recognition and second trained model into facts and infer new facts using steam reasoning. Matching patterns comprised in form of an adaptable business logic with inferred new facts and trigger alerts, if pattern is matched.