Real-time stage-wise object tracking in traffic scenes: an online tracker selection method via deep reinforcement learning

Lu, Xiao; Cao, Yihong; Liu, Sheng; Zhou, Xuanyu; Yang, Yimin

doi:10.1007/s00521-021-06439-z

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Meanwhile, the proposed method outperformed other DL methods under low computational complexity. Lu et al [ 24 ] investigated the online tracker selection based on DRL. Through the real-time segmented target tracking strategy in the traffic scene, they studied how to capture and track the environment's dynamics effectively.…”

Section: Recent Related Workmentioning

confidence: 99%

Landscaping Agricultural and Animal Husbandry Production Park Using Lightweight Deep Reinforcement Learning under Circular Symbiosis Concept

Cui¹

2022

Computational Intelligence and Neuroscience

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The paper intends to optimize the landscape of the agricultural and animal husbandry (AG and AH) production park using the deep reinforcement learning (DRL) model under circular symbiosis. Therefore, after reviewing the relevant literature, decision tree evolutionary algorithm, and ensemble learning criteria, this paper studies and constructs the circular symbiotic industrial chain. Then, an experiment of landscaping the park and optimizing the production is made with full consideration of practical institutions. Finally, the numerical results show that the yield of several crops has been significantly improved after the landscape optimization by the proposed DRL model. Remarkably, the increase in rice yield is the most prominent. The yield of rice and wheat was about 12 kg before optimization and 18 kg after DRL model optimization, which has increased by 6 kg. This research has important reference value for improving the output efficiency of AG and AH products.

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Section: Recent Related Workmentioning

confidence: 99%

Landscaping Agricultural and Animal Husbandry Production Park Using Lightweight Deep Reinforcement Learning under Circular Symbiosis Concept

Cui¹

2022

Computational Intelligence and Neuroscience

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Learning task-specific discriminative representations for multiple object tracking

Nie

Zhu

et al. 2022

Neural Comput & Applic

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Real-time stage-wise object tracking in traffic scenes: an online tracker selection method via deep reinforcement learning

Cited by 2 publications

References 32 publications

Landscaping Agricultural and Animal Husbandry Production Park Using Lightweight Deep Reinforcement Learning under Circular Symbiosis Concept

Landscaping Agricultural and Animal Husbandry Production Park Using Lightweight Deep Reinforcement Learning under Circular Symbiosis Concept

Learning task-specific discriminative representations for multiple object tracking

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