Detection and classification of moving objects in closed spaces using artificial vision algorithms
DOI:
https://doi.org/10.70998/itistct.v2i2.82Keywords:
computer vision, OpenCV, image processing, pets, object detectionAbstract
The field of computer-assisted vision has expanded over the years, awakening great interest in its study and research development in different disciplines to achieve the highest performance of the applications developed with these methods.
This article summarizes the scope of a larger investigation, presented as a Master's Final Project carried out by the same author at the Universitat Politècnica de València (Hidrobo, 2018) in which some of the main techniques of computer vision video surveillance and its behavior when the guarded covered environment is permanently inhabited by a pet. In this way, with the review of different alternatives, a difference is made between an alert generated by its movement and one generated by an intruder, preventing the alarms from being triggered unwantedly.
For this purpose, two video resources were used: 1) the OpenCV computer vision library in a C # environment in order to be able to repeat the experiments in the same circumstances for the different algorithms to be evaluated and, 2) different videos that register animals and people, simulating the environment to be monitored.
The objective of this work was to develop a test bench that allows detecting and classifying a moving object inside a closed environment through the use of image processing and classification algorithms.
At the end of the experiments, the data obtained were arranged in tables that show the results when executing different video sequences on the test bench, using all the image processing and classification methods developed.
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