Autonomous driving is an emerging technology attracting interests from various sectors in recent years.Most of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate in...Autonomous driving is an emerging technology attracting interests from various sectors in recent years.Most of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent modules.In this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving techniques.We first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for vehicles.Following this,a cooperative intelligence framework is proposed for autonomous driving systems.This general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.展开更多
The understanding and analysis of video content are fundamentally important for numerous applications,including video summarization,retrieval,navigation,and editing.An important part of this process is to detect salie...The understanding and analysis of video content are fundamentally important for numerous applications,including video summarization,retrieval,navigation,and editing.An important part of this process is to detect salient (which usually means important and interesting) objects in video segments.Unlike existing approaches,we propose a method that combines the saliency measurement with spatial and temporal coherence.The integration of spatial and temporal coherence is inspired by the focused attention in human vision.In the proposed method,the spatial coherence of low-level visual grouping cues (e.g.appearance and motion) helps per-frame object-background separation,while the temporal coherence of the object properties (e.g.shape and appearance) ensures consistent object localization over time,and thus the method is robust to unexpected environment changes and camera vibrations.Having developed an efficient optimization strategy based on coarse-to-fine multi-scale dynamic programming,we evaluate our method using a challenging dataset that is freely available together with this paper.We show the effectiveness and complementariness of the two types of coherence,and demonstrate that they can significantly improve the performance of salient object detection in videos.展开更多
This paper proposes a novel airport detection method,which integrates the texture features and shape features of the airport.Eight texture features,such as the mean of the region,the deviation of the region,the smooth...This paper proposes a novel airport detection method,which integrates the texture features and shape features of the airport.Eight texture features,such as the mean of the region,the deviation of the region,the smoothness of the region,the skewness of a histogram,the uniformity of the region,the randomness of the region,the mean of the gradient image and the deviation of the gradient image,are used to represent the features of the region.In this method,first the long lines are detected and the regions where the lines locate are segmented.Second,support vector machine(SVM)based on Gaussian kernel is used as a classifier which discriminates the runway from other candidate regions.Experimental results show that the error rate of the proposed method is lower than those of conventional methods which detect airport only by the shape feature of runway.The detection accuracy of the proposed method is nearly ten times higher than that of Liu’s methods,and the method has favorable speed for a real-time system.展开更多
基金in part supported by the Ministry National Key Research and Development Project under Grant 2017YFE0121400the Major Project from Beijing Municipal Science and Technology Commission under Grant Z181100003218007the National Natural Science Foundation of China under Grants 61622101 and 61571020
文摘Autonomous driving is an emerging technology attracting interests from various sectors in recent years.Most of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent modules.In this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving techniques.We first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for vehicles.Following this,a cooperative intelligence framework is proposed for autonomous driving systems.This general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.
基金supported by the National Natural Science Foundation of China(60635050 and 90820017)the National Basic Research Program of China(2007CB311005)
文摘The understanding and analysis of video content are fundamentally important for numerous applications,including video summarization,retrieval,navigation,and editing.An important part of this process is to detect salient (which usually means important and interesting) objects in video segments.Unlike existing approaches,we propose a method that combines the saliency measurement with spatial and temporal coherence.The integration of spatial and temporal coherence is inspired by the focused attention in human vision.In the proposed method,the spatial coherence of low-level visual grouping cues (e.g.appearance and motion) helps per-frame object-background separation,while the temporal coherence of the object properties (e.g.shape and appearance) ensures consistent object localization over time,and thus the method is robust to unexpected environment changes and camera vibrations.Having developed an efficient optimization strategy based on coarse-to-fine multi-scale dynamic programming,we evaluate our method using a challenging dataset that is freely available together with this paper.We show the effectiveness and complementariness of the two types of coherence,and demonstrate that they can significantly improve the performance of salient object detection in videos.
基金supported by the National Natural Science Foundation of China(Grant No.60175006).
文摘This paper proposes a novel airport detection method,which integrates the texture features and shape features of the airport.Eight texture features,such as the mean of the region,the deviation of the region,the smoothness of the region,the skewness of a histogram,the uniformity of the region,the randomness of the region,the mean of the gradient image and the deviation of the gradient image,are used to represent the features of the region.In this method,first the long lines are detected and the regions where the lines locate are segmented.Second,support vector machine(SVM)based on Gaussian kernel is used as a classifier which discriminates the runway from other candidate regions.Experimental results show that the error rate of the proposed method is lower than those of conventional methods which detect airport only by the shape feature of runway.The detection accuracy of the proposed method is nearly ten times higher than that of Liu’s methods,and the method has favorable speed for a real-time system.
