Open AccessObject detection and tracking in video sequences: formalization, metrics and results
Author(s) -
Р. П. Богуш,
Сергей Владимирович Абламейко
Publication year - 2021
Publication title -
informatika
Language(s) - English
Resource type - Journals
eISSN - 2617-6963
pISSN - 1816-0301
DOI - 10.37661/1816-0301-2021-18-1-43-60
Subject(s) - artificial intelligence , computer vision , computer science , tracking (education) , video tracking , frame (networking) , convolutional neural network , object detection , generalization , object (grammar) , trajectory , artificial neural network , object class detection , tracking system , pattern recognition (psychology) , face detection , kalman filter , mathematics , facial recognition system , psychology , telecommunications , pedagogy , mathematical analysis , physics , astronomy
One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the detection and tracking of one and many objects in video. The following metrics are considered: the quality of detection of tracked objects, the accuracy of determining the location of the object in a frame, the trajectory of movement, the accuracy of tracking multiple objects. Based on the considered generalization, an algorithm for tracking people has been developed that uses the tracking through detection method and convolutional neural networks to detect people and form features. Neural network features are included in a composite descriptor that also contains geometric and color features to describe each detected person in the frame. The results of experiments based on the considered criteria are presented, and it is experimentally confirmed that the improvement of the detector operation makes it possible to increase the accuracy of tracking objects. Examples of frames of processed video sequences with visualization of human movement trajectories are presented.