Research on the improvement of vision target tracking algorithm for Internet of things technology and Simple extended application in pellet ore phase

Li, Jie; Zhi, Jian-Ming; Hu, Wei; Li-ya, Wang; Yang, Aimin

doi:10.1016/j.future.2020.04.014

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The newly developed object-recognition algorithms, grounded in deep learning, are able to automatically learn and extract features. Moreover, they can be adapted to complex scenarios, rendering them particularly well-suited for intelligent monitoring applications in logistics warehouses [6,7].…”

Section: Introductionmentioning

confidence: 99%

Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT

Xie,

Yao

2023

Applied Sciences

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The future development of Industry 4.0 places paramount importance on human-centered/-centric factors in the production, design, and management of logistic systems, which has led to the emergence of Industry 5.0. However, effectively integrating human-centered/-centric factors in logistics scenarios has become a challenge. A pivotal technological solution for dealing with such a challenge is to distinguish and track moving objects such as humans and goods. Therefore, an algorithm model combining YOLOv5 and DeepSORT for logistics warehouse object tracking is designed, where YOLOv5 is selected as the object-detection algorithm and DeepSORT distinguishes humans from goods and environments. The evaluation metrics from the MOT Challenge affirm the algorithm’s robustness and efficacy. Through rigorous experimental tests, the combined algorithm demonstrates rapid convergence (within 30 ms), which holds promising potential for applications in real-world logistics warehouses.

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Section: Introductionmentioning

confidence: 99%

Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT

Xie,

Yao

2023

Applied Sciences

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“…Visual object tracking (VOT) has emerged as a dynamic study area due to its utilization in a wide range of applications such as human action recognition [ 1 , 2 , 3 ], traffic monitoring [ 4 , 5 ], pellet ore phase [ 6 ], smart city [ 7 ], embedded system [ 8 ], surveillance [ 9 , 10 , 11 ] and medical diagnosis [ 12 , 13 ]. While significant progress has been made in recent years, accurate estimation for tracking an object is still a challenge in a video sequence due to various factors such as scale variations, occlusion, deformation, background clutters, to name a few [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Mehmood

Jalil

Ali

et al. 2021

Sensors

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Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

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Analysis of high-level dance movements under deep learning and internet of things

Wang

Tong

2022

J Supercomput

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Research on the improvement of vision target tracking algorithm for Internet of things technology and Simple extended application in pellet ore phase

Cited by 4 publications

References 19 publications

Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT

Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT

Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Analysis of high-level dance movements under deep learning and internet of things

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