The key issues for roadside sensing system(RSS)include achieving accuracy and real-time sharing of over-horizon perception information.This study proposes a novel and efficient framework dedicated to multi-object dete...The key issues for roadside sensing system(RSS)include achieving accuracy and real-time sharing of over-horizon perception information.This study proposes a novel and efficient framework dedicated to multi-object detection from the roadside perspective.Firstly,compared to other backbones,the mobile net-based model has superior performance and speed as results of the network parameters obtained from network architecture search(NAS),developed to increase the forward inference speed.Secondly,a method of optimization based on the coordinate attention mechanism is developed to increase the longrange dependence of neural networks on spatial information.Thirdly,the traditional convolution operation in the attention mechanism is optimized by the depthwise over-parameterized convolution(DOPC)to improve the capability of extracting features from high-dimensional feature space.Finally,the lightweight single-stage multi-target detection model from the roadside perspective based on DCM3-YOLOv4 is developed.The test results show that the optimized one-stage lightweight multiple object detection model DCM3-YOLOv4 on the RS-UA dataset produces a mean average precision(mAP)value of 0.930 and a network model with parameter size of 31.12 Million.The inference time is 96.13 ms,which is faster than another basic model on the same platform.The proposed methods can be utilized in a wide range of applications,where the accuracy and speed requirements of RSS must be met.展开更多
Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential...Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential and challenging task.In the previous studies,many vehicle detection methods have been presented.These proposed approaches mostly used either motion information or characteristic information to detect vehicles.Although these methods are effective in detecting vehicles,their detection accuracy still needs to be improved.Moreover,the headlights and windshields,which are used as the vehicle features for detection in these methods,are easily obscured in some traffic conditions.The paper aims to discuss these issues.Design/methodology/approach-First,each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model.Next,the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks.These feature blocks will be used to track the moving objects frame by frame.Findings-Using the proposed method,it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement(waving trees),which has to be deemed as background.In addition,the proposed method is able to deal with different vehicle shapes such as cars,vans,and motorcycles.Originality/value-This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.展开更多
基金supported in part by the National Natural Science Foundation of China(52072333,52202503)Science and Technology Project of Hebei Education Department(BJK2023026)Hebei Natural Science Foundation(F2022203054).
文摘The key issues for roadside sensing system(RSS)include achieving accuracy and real-time sharing of over-horizon perception information.This study proposes a novel and efficient framework dedicated to multi-object detection from the roadside perspective.Firstly,compared to other backbones,the mobile net-based model has superior performance and speed as results of the network parameters obtained from network architecture search(NAS),developed to increase the forward inference speed.Secondly,a method of optimization based on the coordinate attention mechanism is developed to increase the longrange dependence of neural networks on spatial information.Thirdly,the traditional convolution operation in the attention mechanism is optimized by the depthwise over-parameterized convolution(DOPC)to improve the capability of extracting features from high-dimensional feature space.Finally,the lightweight single-stage multi-target detection model from the roadside perspective based on DCM3-YOLOv4 is developed.The test results show that the optimized one-stage lightweight multiple object detection model DCM3-YOLOv4 on the RS-UA dataset produces a mean average precision(mAP)value of 0.930 and a network model with parameter size of 31.12 Million.The inference time is 96.13 ms,which is faster than another basic model on the same platform.The proposed methods can be utilized in a wide range of applications,where the accuracy and speed requirements of RSS must be met.
文摘Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential and challenging task.In the previous studies,many vehicle detection methods have been presented.These proposed approaches mostly used either motion information or characteristic information to detect vehicles.Although these methods are effective in detecting vehicles,their detection accuracy still needs to be improved.Moreover,the headlights and windshields,which are used as the vehicle features for detection in these methods,are easily obscured in some traffic conditions.The paper aims to discuss these issues.Design/methodology/approach-First,each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model.Next,the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks.These feature blocks will be used to track the moving objects frame by frame.Findings-Using the proposed method,it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement(waving trees),which has to be deemed as background.In addition,the proposed method is able to deal with different vehicle shapes such as cars,vans,and motorcycles.Originality/value-This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.
