期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：12
DOI：10.14569/IJACSA.2021.01212107
语种：English
出版社：Science and Information Society (SAI)
摘要：Computer Vision has set a new trend in image resolution, object detection, object tracking, and more by incor-porating advanced techniques from Artificial Intelligence (AI). Object detection and tracking have many use cases such as driverless cars, security systems, patient monitoring, and so on. Various methods have been proposed to overcome the challenges such as long-term occlusion, identity switching, and fragmenta-tion in real-time multi-object detection and tracking. However, reducing the number of identity switches and fragmentation remains unclear in multi-object detection and tracking. Hence, in this paper, we proposed a multi-object detection and tracking technique that involves two stages. The first stage helps to detect the multiple objects with high uniqueness using Faster RCNN and the second stage, Improved Sqrt cosine similarity, helps to track the multiple objects by using appearance and motion features. Finally, we evaluated our proposed technique using the Multi-Object Tracking (MOT) benchmark dataset with current state-of-the-art methods. The proposed technique resulted in enhanced accuracy and reduces identity switching and fragmentation.
关键词：Multi-object detection; tracking; faster RCNN; con-volution neural network; data association
