Open AccessObject Tracking by Detection using YOLO and SORT
Author(s) -
Heet Thakkar,
Noopur Tambe,
Sanjana Thamke,
Vaishali K. Gaidhane Prof.
Publication year - 2020
Publication title -
international journal of scientific research in computer science, engineering and information technology
Language(s) - English
Resource type - Journals
ISSN - 2456-3307
DOI - 10.32628/cseit206256
Subject(s) - sort , computer vision , artificial intelligence , computer science , video tracking , tracking (education) , object detection , object (grammar) , process (computing) , domain (mathematical analysis) , eye tracking , pattern recognition (psychology) , information retrieval , mathematics , psychology , mathematical analysis , pedagogy , operating system
Over the past two decades, computer vision has received a great deal of coverage. Visual object tracking is one of the most important areas of computer vision. Tracking objects is the process of tracking over time a moving object (or several objects). The purpose of visual object tracking in consecutive video frames is to detect or connect target objects. In this paper, we present analysis of tracking-by-detection approach which include detection by YOLO and tracking by SORT algorithm. This paper has information about custom image dataset being trained for 6 specific classes using YOLO and this model is being used in videos for tracking by SORT algorithm. Recognizing a vehicle or pedestrian in an ongoing video is helpful for traffic analysis. The goal of this paper is for analysis and knowledge of the domain.