Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning

Jiang, Mingxin; Deng, Chao; Pan, Zhigeng; Wang, Lan-fang; Sun, Xing

doi:10.1155/2018/4695890

Cited by 29 publications

(19 citation statements)

References 48 publications

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“…Frames per Second Siamese CNN [13] 2.1 RNN-LSTM [14] 166.8 JPDA [29] 35.6 LSTM-DRL [18] 108 YOLO-Based (This study) 207.6…”

Section: Methodsmentioning

confidence: 99%

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Online Multi-Object Tracking in Videos Based on Features Detected by YOLO

Al-Arbo¹

2021

TURCOMAT

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With the rapid development of different applications that rely on multi-object detection and tracking, significant attention has been brought toward improving the performance of these methods. Recently, Artificial Neural Networks (ANNs) have shown outstanding performance in different applications, where objects detection and tracking are no exception. In this paper, we proposed a new object tracking method based on descriptors extracted using the convolutional filters of the YOLOv3 neural network. As these features are detected and processed during the detection phase, the proposed method has exploited these features to produce efficient and robust descriptors. The proposed method has shown better performance, compared to state-of-the-art methods, by producing better predictions using less computations. The evaluation results show that the proposed method has been able to process an average of 207.6 frames per second to track objects with 67.6% Multi-Object Tracking Accuracy (MOTA) and 89.1% Multi-Object Tracking Precision (MOTP).

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“…Frames per Second Siamese CNN [13] 2.1 RNN-LSTM [14] 166.8 JPDA [29] 35.6 LSTM-DRL [18] 108 YOLO-Based (This study) 207.6…”

Section: Methodsmentioning

confidence: 99%

“…Jiang et al [18] propose another tracking method that relies on ANNs, specifically using Long-Short-Term Memory (LSTM) neurons. These neurons feedback their output at a certain time instance into their inputs at the next time instance.…”

Section: Related Workmentioning

confidence: 99%

Online Multi-Object Tracking in Videos Based on Features Detected by YOLO

Al-Arbo¹

2021

TURCOMAT

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“…The principle of DRL is illustrated in Figure 4(d [56], asynchronous actor-critic (A3C) [57], duelling DQN [58], double DQN [59], and multiagent DRL [60]. Researchers are still working to improve the DRL method by integrating LSTM [61] or CNN [62] with it and utilizing the advantages of both architectures in the same network.…”

Section: ) Deep Reinforcement Learning (Drl)mentioning

confidence: 99%

A Survey on Applications of Deep Learning in Cloud Radio Access Network

Rodoshi

Choi

2021

IEEE Access

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The necessity for high-speed and low-latency connectivity of the vast number of mobile users is rising with the immense usage of mobile applications. A cloud radio access network (C-RAN) is a promising framework for next-generation cellular communication, which can satisfy the requirements of significantly increasing data traffic and user demands. In C-RAN, the data processing unit can be centralized and virtualized in data centers and can be shared among distributed base stations. Deep learning (DL) appears to be a feasible approach for facilitating the data processing capability, resource management in the cloud, and predicting dynamic traffic in cellular communication. The convergence of C-RAN and DL is expected to bring new prospects to both interdisciplinary research and industrial applications. In this regard, different approaches have been proposed for DL-based C-RAN in the literature. This article provides a comprehensive survey of the state-of-the-art DL techniques applied in C-RAN. A brief introduction of the C-RAN architecture and DL techniques is given to provide insights into these two emerging technologies. Existing surveys are also discussed to highlight the research gap. The reviewed works are categorized into power consumption optimization, network performance maximization, and QoS maximization based on their optimization objectives. The key ideas of DL applied in the reviewed schemes are also mentioned, and the performance evaluation techniques used in the research are discussed and compared. Lastly, research challenges and open research issues are highlighted to provide future research directions.INDEX TERMS Cloud radio access network, deep learning, evaluation techniques, optimization objectives, performance metrics.

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“…A Markov Decision Process (MDP) formally describes a framework for RL. The MDP is a mathematical abstraction that is defined by a tuple

in which

is a set of states,

is a set of actions,

is a reward function, and

is a transition probability function [ 39 ]. In a general condition-based maintenance management setting, the state is the health condition of the asset.…”

Section: The Rl Maintenance Schedulermentioning

confidence: 99%

Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion

Mahmoodzadeh

Droguett

et al. 2020

Sensors

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Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes large risks such as ruptures and leakage to the environment and the pipeline system. Therefore, various maintenance actions are performed routinely to ensure the integrity of the pipelines. The costs of the corrosion-related maintenance actions are a significant portion of the pipeline’s operation and maintenance costs, and minimizing this large cost is a highly compelling subject that has been addressed by many studies. In this paper, we investigate the benefits of applying reinforcement learning (RL) techniques to the corrosion-related maintenance management of dry gas pipelines. We first address the rising need for a simulated testbed by proposing a test bench that models corrosion degradation while interacting with the maintenance decision-maker within the RL environment. Second, we propose a condition-based maintenance management approach that leverages a data-driven RL decision-making methodology. An RL maintenance scheduler is applied to the proposed test bench, and the results show that applying the proposed condition-based maintenance management technique can reduce up to 58% of the maintenance costs compared to a periodic maintenance policy while securing pipeline reliability.

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Multiobject Tracking in Videos Based on LSTM and Deep Reinforcement Learning

Cited by 29 publications

References 48 publications

Online Multi-Object Tracking in Videos Based on Features Detected by YOLO

Online Multi-Object Tracking in Videos Based on Features Detected by YOLO

A Survey on Applications of Deep Learning in Cloud Radio Access Network

Condition-Based Maintenance with Reinforcement Learning for Dry Gas Pipeline Subject to Internal Corrosion

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