Vision-Based Human Detection Techniques: A Descriptive Review

Sumit, Shahriar Shakir; Mirjalili, Seyedali

doi:10.1109/access.2021.3063028

Cited by 18 publications

(15 citation statements)

References 303 publications

(356 reference statements)

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“…References [88,114,125,126] did not discuss the research progress in the past two years due to time constraints and rarely involved the current research focus on deep learning techniques. Reference [127] mainly discusses Human Detection technology and does not make detailed analysis for pedestrian detection.…”

Section: Discussionmentioning

confidence: 99%

[Retracted] A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

Tian

Yong

Wang

et al. 2021

Computational Intelligence and Neuroscience

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Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real-time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.

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Section: Discussionmentioning

confidence: 99%

[Retracted] A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

Tian

Yong

Wang

et al. 2021

Computational Intelligence and Neuroscience

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“…As a result, even experienced doctors may have problems with diagnosis and miss some ADs, which will delay the needed treatments. Therefore, to achieve accurate AD diagnosis automatically and efficiently, deep learning approaches like CNN can been used [60]. As the resolution of CT images and various AD shapes can bring in great classification difficulties, this classification problem is suitable for testing the CNNs obtained by SHEDA.…”

Section: Case Study On Ad Diagnosismentioning

confidence: 99%

Surrogate-Assisted Hybrid-Model Estimation of Distribution Algorithm for Mixed-Variable Hyperparameters Optimization in Convolutional Neural Networks

Zhan

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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The performance of a convolutional neural network (CNN) heavily depends on its hyperparameters. However, finding a suitable hyperparameters configuration is difficult, challenging, and computationally expensive due to three issues, which are 1) the mixed-variable problem of different types of hyperparameters; 2) the large-scale search space of finding optimal hyperparameters; and 3) the expensive computational cost for evaluating candidate hyperparameters configuration. Therefore, this article focuses on these three issues and proposes a novel estimation of distribution algorithm (EDA) for efficient hyperparameters optimization, with three major contributions in the algorithm design. First, a hybrid-model EDA is proposed to efficiently deal with the mixed-variable difficulty. The proposed algorithm uses a mixed-variable encoding scheme to encode the mixed-variable hyperparameters and adopts an adaptive hybrid-model learning (AHL) strategy to efficiently optimize the mixed-variables. Second, an orthogonal initialization (OI) strategy is proposed to efficiently deal with the challenge of large-scale search space. Third, a surrogate-assisted multi-level evaluation (SME) method is proposed to reduce the expensive computational cost. Based on the above, the proposed algorithm is named surrogateassisted hybrid-model EDA (SHEDA). For experimental studies,

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“…People detection has involved the development of a large set of solutions over the years. A very popular solution is to use cameras to detect people via feature extraction algorithms, from the well-known extractors SURF (speeded-up robust features) or SIFT (scale invariant feature transform) to hog (histogram of oriented gradients) solutions [17]. However, in recent years, machine learning and deep learning-based approaches have achieved overwhelming results for object and people detection.…”

Section: Related Workmentioning

confidence: 99%

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

2023

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Safe navigation is a key objective for autonomous applications, particularly those involving mobile tasks, to avoid dangerous situations and prevent harm to humans. However, the integration of a risk management process is not yet mandatory in robotics development. Ensuring safety using mobile robots is critical for many real-world applications, especially those in which contact with the robot could result in fatal consequences, such as agricultural environments where a mobile device with an industrial cutter is used for grass-mowing. In this paper, we propose an explicit integration of a risk management process into the design of the software for an autonomous grass mower, with the aim of enhancing safety. Our approach is tested and validated in simulated scenarios that assess the effectiveness of different custom safety functionalities in terms of collision prevention, execution time, and the number of required human interventions.

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Vision-Based Human Detection Techniques: A Descriptive Review

Cited by 18 publications

References 303 publications

[Retracted] A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

[Retracted] A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning

Surrogate-Assisted Hybrid-Model Estimation of Distribution Algorithm for Mixed-Variable Hyperparameters Optimization in Convolutional Neural Networks

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

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