Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker

Jiang, Mingxin; Tao, Hai; Pan, Zhigeng; Wang, Haiyan; Jia, Yongxing; Deng, Chao

doi:10.1109/access.2019.2901300

Cited by 42 publications

(19 citation statements)

References 37 publications

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“…To solve this problem, they used an auto-referee system and the occlusion problem by using CNN with the appearance feature, which also uses the spatial attention mechanism of insertion and location. Jiang et al proposed multi-agent deep reinforcement learning (MADRL) [138], which uses a learning method that uses Q-Learning (QL) to treat other agents as part of the current agent's environment. [56], which included a deformable attention to transformer encoder.…”

Section: Automatic Detection Learningmentioning

confidence: 99%

Multiple Object Tracking in Deep Learning Approaches: A Survey

et al. 2021

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Object tracking is a fundamental computer vision problem that refers to a set of methods proposed to precisely track the motion trajectory of an object in a video. Multiple Object Tracking (MOT) is a subclass of object tracking that has received growing interest due to its academic and commercial potential. Although numerous methods have been introduced to cope with this problem, many challenges remain to be solved, such as severe object occlusion and abrupt appearance changes. This paper focuses on giving a thorough review of the evolution of MOT in recent decades, investigating the recent advances in MOT, and showing some potential directions for future work. The primary contributions include: (1) a detailed description of the MOT’s main problems and solutions, (2) a categorization of the previous MOT algorithms into 12 approaches and discussion of the main procedures for each category, (3) a review of the benchmark datasets and standard evaluation methods for evaluating the MOT, (4) a discussion of various MOT challenges and solutions by analyzing the related references, and (5) a summary of the latest MOT technologies and recent MOT trends using the mentioned MOT categories.

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Section: Automatic Detection Learningmentioning

confidence: 99%

Multiple Object Tracking in Deep Learning Approaches: A Survey

et al. 2021

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“…This action, a t , produces an effect on the environment. This fact is observed by the interpreter who provides information to the agent about the new state, s t+1 , and the reward of the previous action, r t+1 , closing the loop [38,39]. Some authors consider that the interpreter is embedded in either the environment or the agent; in any case, the function of the interpreter is always present.…”

Section: Rl-inspired Controllermentioning

confidence: 99%

Exploring Reward Strategies for Wind Turbine Pitch Control by Reinforcement Learning

Sierra-García

Santos

2020

Applied Sciences

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In this work, a pitch controller of a wind turbine (WT) inspired by reinforcement learning (RL) is designed and implemented. The control system consists of a state estimator, a reward strategy, a policy table, and a policy update algorithm. Novel reward strategies related to the energy deviation from the rated power are defined. They are designed to improve the efficiency of the WT. Two new categories of reward strategies are proposed: “only positive” (O-P) and “positive-negative” (P-N) rewards. The relationship of these categories with the exploration-exploitation dilemma, the use of ϵ-greedy methods and the learning convergence are also introduced and linked to the WT control problem. In addition, an extensive analysis of the influence of the different rewards in the controller performance and in the learning speed is carried out. The controller is compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. The simulations show how the P-N rewards improve the performance of the controller, stabilize the output power around the rated power, and reduce the error over time.

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“…Our pre-trained human detector YOLOv3 [36,40,41] (Table 1) is configured to obtain for almost everyone (human) in the image (I i ) a bounding box that surrounds him. Considering a video V subdivided into n frames as…”

Section: Human Detector and Regions Of Interest (Rois)mentioning

confidence: 99%

Novel Hand Gesture Alert System

et al. 2019

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. Sexual assault can cause great societal damage, with negative socio-economic, mental, sexual, physical and reproductive consequences. According to the Eurostat, the number of crimes increased in the European Union between 2008 and 2016. However, despite the increase in security tools such as cameras, it is usually difficult to know if an individual is subject to an assault based on his or her posture. Hand gestures are seen by many as the natural means of nonverbal communication when interacting with a computer, and a considerable amount of research has been performed. In addition, the identifiable hand placement characteristics provided by modern inexpensive commercial depth cameras can be used in a variety of gesture recognition-based systems, particularly for human-machine interactions. This paper introduces a novel gesture alert system that uses a combination of Convolution Neural Networks (CNNs). The overall system can be subdivided into three main parts: firstly, the human detection in the image using a pretrained “You Only Look Once (YOLO)” method, which extracts the related bounding boxes containing his/her hands; secondly, the gesture detection/classification stage, which processes the bounding box images; and thirdly, we introduced a module called “counterGesture”, which triggers the alert.

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Multi-Agent Deep Reinforcement Learning for Multi-Object Tracker

Cited by 42 publications

References 37 publications

Multiple Object Tracking in Deep Learning Approaches: A Survey

Multiple Object Tracking in Deep Learning Approaches: A Survey

Exploring Reward Strategies for Wind Turbine Pitch Control by Reinforcement Learning

Novel Hand Gesture Alert System

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