Multi‐receptive field graph convolutional neural networks for pedestrian detection

Shen, Chao; Zhao, Xiangmo; Fan, Xing; Lian, Xinyu; Zhang, Fan; Kreidieh, Abdul Rahman; Liu, Zhanwen

doi:10.1049/iet-its.2018.5618

Cited by 17 publications

(16 citation statements)

References 36 publications

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“…Liu et al proposed to use GNN as the person re-identification model within and outside of the frame [41]. Shen et al proposed multi-receptive field GCN of body parts graphs to enhance single-shot pedestrian detection [42]. Zhang et al proposed that the GNN can be used for evaluating social relationships in pedestrian trajectory prediction.…”

Section: Related Workmentioning

confidence: 99%

Contrastive learning of graph encoder for accelerating pedestrian trajectory prediction training

Yao

Ding

2021

IET Image Processing

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In the area of pedestrian trajectory prediction, the hybrid structures of temporal feature extractor or spatial feature extractor have paved the way for the precise prediction model, and they are in larger and larger scale. Learning of specific feature encoding model not only influenced by the structure of the network, but also by the learning manners such as supervised learning and unsupervised learning. Previous works concentrated on more comprehensive encoders and more delicate designs of feature extractors. However, the mutual influence factors from the neighbour pedestrians associate with the distance to the centre pedestrian seldomly noticed. Most of the existed feature extractors in prediction models trained in the way of supervised learning other than unsupervised manners caused the problem that the extracted features are always handcrafted without the natural distinction of obscure situations. The graph contrastive accelerating encoder is proposed, which accelerates the pedestrian trajectory prediction training process of the state of the art method of spatio-temporal graph transformer networks. Employing the unsupervised contrastive learning process and the graph of neighbours representing distance affection of nearest and farthest pedestrian to the centre pedestrian, the graph contrastive accelerating encoder significantly shrinked the training time. Holding the final performance on to state of the art level, the proposed method let the lowest pedestrian trajectory prediction error show up in the obviously earlier training steps.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

Contrastive learning of graph encoder for accelerating pedestrian trajectory prediction training

Yao

Ding

2021

IET Image Processing

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“…So, the pedestrian detection methods based on deep learning have been studied extensively. For example, Shen et al [81] proposed a single-shot pedestrian detection method using a multi-receptive field-based framework. The framework of the pedestrian detection in [81] is shown in Figure 20.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

“…For example, Shen et al [81] proposed a single-shot pedestrian detection method using a multi-receptive field-based framework. The framework of the pedestrian detection in [81] is shown in Figure 20. First, the image is used as the input of the Visual Geometry Group (VGG) network.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

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A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

110

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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“…To achieve a higher level of intelligent driving, the self-driving car needs to understand the high-level semantic information of its location, to make the decision of driving strategy and path planning. For example, the car should slow down near the school, pay attention to the use of anti-skid mode/function in rainy and snowy weather, keep driving at high speed on the highway, etc [14], [15].…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

Shen

Chen

et al. 2022

IEEE Trans. Instrum. Meas.

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Self-driving car is a hot research topic in the field of intelligent transportation system, which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is one of the key technologies of self-driving cars, which can provide the basis for decision-making in self-driving cars. In recent years, deep learning-based solutions have achieved good results in the problem of scene classification. However, some problems should be further studied in the scene classification methods, such as how to deal with the similarities among different categories and the differences among the same category. To deal with these problems, an improved deep network-based scene classification method is proposed in this paper. In the proposed method, an improved Faster RCNN (Regions with CNN features) network is used to extract the features of representative objects in the scene to obtain local features, where a new residual attention block is added to the Faster RCNN network to highlight local semantics related to driving scenarios. In addition, an improved Inception module is used to extract global features, where a mixed Leaky ReLU and ELU function is presented, to reduce the possible redundancy of the convolution kernel and enhance the robustness. Then the local features and the global features are fused to realize the scene classification.Finally, a private data set is built from the public data sets for the specialized application of scene classification in the self-driving field and the proposed method is tested on the proposed data set. The experimental results show that the accuracy of the proposed method can reach 94.76%, which is higher than the state-of-the-art methods.

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Multi‐receptive field graph convolutional neural networks for pedestrian detection

Cited by 17 publications

References 36 publications

Contrastive learning of graph encoder for accelerating pedestrian trajectory prediction training

Contrastive learning of graph encoder for accelerating pedestrian trajectory prediction training

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

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