Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems

Chen, Xu; Liu, Lei; Deng, Yubin; Kong, Xiangyu

doi:10.1007/s11082-019-1977-7

Cited by 14 publications

(5 citation statements)

References 24 publications

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“…In order to obtain better detection results, partial characteristics of pedestrians can be tested. 18 The head and shoulders of pedestrians are similar to Ω shape and are relatively stable. 19 In this paper, the head and shoulder are used as the detection target in the first level detection.…”

Section: Methodsmentioning

confidence: 99%

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Zhang

2022

Measurement and Control

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The research of pedestrian target detection in complex scenes is still of great significance. Aiming at the problem of high missed detection rate and poor timeliness of pedestrian target detection in complex scenes. This paper proposes an improved classification method. First, Haar features were extracted from the images to be detected, and the candidate areas of pedestrians were determined by Adaboost classifier. Then, the traditional SVM classifier was improved by using the combined kernel function instead of the single kernel function, and the optimal proportion of each function in the combined kernel function was found by using the adaptive particle swarm optimization algorithm. Finally, the improved SVM classifier was combined with the fusion feature to further detect the candidate area to accurately locate the pedestrian’s position. Experimental results show that compared with the traditional detection framework, the proposed method can effectively improve the detection speed and the detection accuracy. This method has certain practical significance for pedestrian target detection in complex scenes.

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Section: Methodsmentioning

confidence: 99%

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Zhang

2022

Measurement and Control

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“…Experiments show that the algorithm can effectively improve the practicability of image information processing [14]. By simulating the "posture trend diagram" in the vehicle image, Xu Chen realizes the position reference evaluation basis in the vehicle image, and improves the real-time update efficiency of the traditional vehicle image processing scheme [15]. To sum up, it can be seen that most of the current vehicle navigation and path planning modes do not involve the intelligent image processing algorithm based on the image information of reference objects around the road conditions, and there is no research on the real-time image acquisition of road conditions and the construction of relevant models for Intelligent Vehicles [16].…”

Section: Introductionmentioning

confidence: 94%

Sparse representation optimization of Gaussian mixed feature of image based on convolution neural network

2022

Soft Comput

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With the rapid development of intelligent algorithm and image processing technology, the limitations of traditional image processing methods are more and more obvious. Based on this, this paper studies a new pattern of sparse representation optimization of image Gaussian mixture feature based on convolution neural network, and designs a sparse representation system model of vehicle detection image based on convolution neural network. The vehicle image data is collected from many aspects, and the convolution neural network is used for comprehensive analysis and evaluation. The model can extract the feature information of the vehicle detection image better by making the scheme of the real-time vehicle detection image and according to the image features and convolution neural network algorithm. The results show that the Gaussian mixture feature sparse representation optimization model based on convolution neural network has the advantages of high feasibility, high data accuracy and high response speed, which can enhance the processing efficiency of vehicle detection image and improve the utilization of local environmental information in the image.

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“…For multi-scale vehicle detection, Zhao et al [ 44 ] proposed an FPES method to enhance the quality of feature pyramids. Chen et al [ 45 ] used the visual attention mechanism to extract the object candidate regions and generated the detection sub-window in them. Then, the vehicle detection was realized by a cascade classifier.…”

Section: Related Workmentioning

confidence: 99%

PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

Sun

Liu

et al. 2022

Sensors

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Pedestrian and vehicle detection plays a key role in the safe driving of autonomous vehicles. Although transformer-based object detection algorithms have made great progress, the accuracy of detection in rainy scenarios is still challenging. Based on the Swin Transformer, this paper proposes an end-to-end pedestrian and vehicle detection algorithm (PVformer) with deraining module, which improves the image quality and detection accuracy in rainy scenes. Based on Transformer blocks, a four-branch feature mapping model was introduced to achieve deraining from a single image, thereby mitigating the influence of rain streak occlusion on the detector performance. According to the trouble of small object detection only by visual transformer, we designed a local enhancement perception block based on CNN and Transformer. In addition, the deraining module and the detection module were combined to train the PVformer model through transfer learning. The experimental results show that the algorithm performed well on rainy days and significantly improved the accuracy of pedestrian and vehicle detection.

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Vehicle detection based on visual attention mechanism and adaboost cascade classifier in intelligent transportation systems

Cited by 14 publications

References 24 publications

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Research on hierarchical pedestrian detection based on SVM classifier with improved kernel function

Sparse representation optimization of Gaussian mixed feature of image based on convolution neural network

PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

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