The Automatic Detection of Pedestrians under the High-Density Conditions by Deep Learning Techniques

Jin, Cheng-Jie; Shi, Xiaomeng; Hui, Ting; Li, Dawei; Ma, Ke

doi:10.1155/2021/1396326

Cited by 13 publications

(6 citation statements)

References 31 publications

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“…This method allows for continuous tracking of a large number of pedestrians using a multi-objective tracker, which facilitates the analysis of individual or group behaviour patterns ( Cao, Sai & Lu, 2020 ). It has been experimentally demonstrated that nearly 1,000 people can be continuously and dynamically identified using deep learning ( Xue & Ju, 2021 ), and the accuracy may be greater than 0.95, even for a pedestrian density of 9.0 per m 2 ( Jin et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Pedestrian walking speed monitoring at street scale by an in-flight drone

Jiao¹,

Teng²

2023

PeerJ Computer Science

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The walking speed of pedestrians is not only a reflection of one’s physiological condition and health status but also a key parameter in the evaluation of the service level of urban facilities and traffic engineering applications, which is important for urban design and planning. Currently, the three main ways to obtain walking speed are based on trails, wearable devices, and images. The first two cannot be popularized in larger open areas, while the image-based approach requires multiple cameras to cooperate in order to extract the walking speed of an entire street, which is costly. In this study, a method for extracting the pedestrian walking speed at a street scale from in-flight drone video is proposed. Pedestrians are detected and tracked by You Only Look Once version 5 (YOLOv5) and Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) algorithms in the video taken from a flying unmanned aerial vehicle (UAV). The distance that pedestrians traveled related to the ground per fixed time interval is calculated using a combined algorithm of Scale-Invariant Feature Transform (SIFT) and random sample consensus (RANSAC) followed by a geometric correction algorithm. Compared to ground truth values, it shows that 90.5% of the corrected walking speed predictions have an absolute error of less than 0.1 m/s. Overall, the method we have proposed is accurate and feasible. A particular advantage of this method is the ability to accurately predict the walking speed of pedestrians without keeping the flight speed of the UAV constant, facilitating accurate measurements by non-specialist technicians. In addition, because of the unrestricted flight range of the UAV, the method can be applied to the entire scale of the street, which assists in a better understanding of how the settings and layouts of urban affect people’s behavior.

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Section: Introductionmentioning

confidence: 99%

Pedestrian walking speed monitoring at street scale by an in-flight drone

Jiao¹,

Teng²

2023

PeerJ Computer Science

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“…Therefore, this paper mainly studies the description language of common business models, and then introduces knowledge engineering technology into the modeling and analysis of multi-objective problems, and puts forward the corresponding intelligent model description language and algorithm generation method. [4][5][6]…”

Section: Introducementioning

confidence: 99%

The characteristics of artificial intelligence model description language and its research direction

Fang

2023

AETR

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As an important branch of the development of computer science, artificial intelligence not only belongs to the basic component of computer science and technology research, but also has been widely used in computer engineering. As the basic environment for the research of artificial intelligence system, it is the focus of Chinese researchers to deeply explore the intelligent model and describe the characteristics and future development direction of language. Therefore, after understanding the development trend of artificial intelligence and intelligent language, this paper mainly studies how to use modeling elements in UML to describe infrastructure, cooperation model, information model and behavior model in business models, and describes language and algorithm generation according to intelligent models oriented to multi-objective problems, providing an effective basis for artificial intelligence language research in the new era.

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“…The experimental results show that the average precisions for pedestrian and vehicle detection improved by 2%∼5% compared with YOLOv3 model. Jin et al [ 11 ] utilized one emerging method based on YOLOv3 in high-density pedestrians detection situations and achieved good results. To improve the near-surface detection performance of UAVs in low illumination environments, Wang et al [ 12 ] proposed a U-type generative adversarial network (GAN) to fuse visible and IR images to generate color fusion images.…”

Section: Introductionmentioning

confidence: 99%

Research on Pedestrian Detection Model and Compression Technology for UAV Images

Liu

Wang

Liu

2022

Sensors

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The large view angle and complex background of UAV images bring many difficulties to the detection of small pedestrian targets in images, which are easy to be detected incorrectly or missed. In addition, the object detection models based on deep learning are usually complex and the high computational resource consumption limits the application scenarios. For small pedestrian detection in UAV images, this paper proposes an improved YOLOv5 method to improve the detection ability of pedestrians by introducing a new small object feature detection layer in the feature fusion layer, and experiments show that the improved method can improve the average precision by 4.4%, which effectively improves the pedestrian detection effect. To address the problem of high computational resource consumption, the model is compressed using channel pruning technology to reduce the consumption of video memory and computing power in the inference process. Experiments show that the model can be compressed to 11.2 MB and the GFLOPs of the model are reduced by 11.9% compared with that before compression under the condition of constant inference accuracy, which is significant for the deployment and application of the model.

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The Automatic Detection of Pedestrians under the High-Density Conditions by Deep Learning Techniques

Cited by 13 publications

References 31 publications

Pedestrian walking speed monitoring at street scale by an in-flight drone

Pedestrian walking speed monitoring at street scale by an in-flight drone

The characteristics of artificial intelligence model description language and its research direction

Research on Pedestrian Detection Model and Compression Technology for UAV Images

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