Suya Li¹, Ying Cao^1,*, Hengyi Ren², Dongsheng Zhu³, Xin Xie¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1449-1470, 2024, DOI:10.32604/cmc.2024.056824 - 15 October 2024

Abstract Multi-object tracking (MOT) has seen rapid improvements in recent years. However, frequent occlusion remains a significant challenge in MOT, as it can cause targets to become smaller or disappear entirely, resulting in low-quality targets, leading to trajectory interruptions and reduced tracking performance. Different from some existing methods, which discarded the low-quality targets or ignored low-quality target attributes. LQTTrack, with a low-quality association strategy (LQA), is proposed to pay more attention to low-quality targets. In the association scheme of LQTTrack, firstly, multi-scale feature fusion of FPN (MSFF-FPN) is utilized to enrich the feature information and assist… More >

Wonseok Jung¹, Se-Han Kim², Seng-Phil Hong³, Jeongwook Seo^4,*

CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 337-348, 2021, DOI:10.32604/cmc.2021.014561 - 12 January 2021

Abstract Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors quickly. In this paper, therefore, we propose a novel monitoring system using an Artificial Intelligence of Things (AIoT) technique combining artificial intelligence and Internet of Things (IoT). The proposed system consists of AIoT edge devices and a central monitoring server. First, an AIoT edge device extracts video frame images from a CCTV camera installed in a pig pen by a frame extraction method, detects multiple pigs in the images More >