Arcabouço para análise de eventos em vídeos.

Abstract

Automatic recognition of relevant events in videos involving sets of actions or interactions between objects can improve surveillance systems, smart cities applications, monitoring of people with physical or mental disabilities, among others. However, designing a framework that can be adapted to several situations without an expert in the involved technologies remains a challenge. In this context, this work is based on the creation of a rule-based generic framework for event detection in video. To create the rules, users form logical expressions using firstorder logic (FOL) and relate the terms with the Allen’s interval algebra, adding a temporal context to the rules. Once it is a framework, it is extensible, and may receive additional modules for performing new detections and inferences. Experimental evaluation was performed using test videos available on Youtube, involving a scenario of traffic with red light crossing events and videos from Camerite website containing parking car events. The focus of the work was not to create object detectors (e.g. cars or people) better than those existing in the state-of-the-art, but, propose and develop a generic and reusable framework that integrates differents computer vision techniques. The accuracy in the detection of the events was within the range of 83.82% and 90.08% with 95% confidence. Obtained maximum accuracy (100 %) in the detection of the events, when replacing the objects detectors by labels manually assigned, what indicated the effectiveness of the inference engine developed for this framework.