Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach t...Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.展开更多
Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless fore...Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.展开更多
A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the di...A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the difference map between an input image and its background and ends at a final contour. An adaptive algorithm was developed to calculate an appropriate energy threshold to control the contours to identify the foreground silhouettes. Experiments show that this method more successfully ignores the nega- tive influence of image noise to obtain an accurate foreground map than other foreground detection algo- rithms. Most shadow pixels are also eliminated by this method.展开更多
In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by fact...In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object.In this paper,we present an approach to detecting the parameters of a moving ship in an inland river.This approach involves interactive calibration without a calibration reference.We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios,and we detect ship length,width,speed,and flow.We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90%for all parameters.展开更多
An estimation approach is proposed in this paper based on the binocular stereovision to collect the degree of crowdedness in public transports. The proposed method combines the disparity with frame differences to extr...An estimation approach is proposed in this paper based on the binocular stereovision to collect the degree of crowdedness in public transports. The proposed method combines the disparity with frame differences to extract the foreground object. An adaptive window normalized cross correlation （NCC） matching and interpolated method is applied to get the sub-pixel image disparity value. Then, the foreground object is projected to the horizontal plane to eliminate the influence of the occlusion and perspective effect. Finally the degree of crowdedness is calculated from the area and the perimeter of the foreground objects. Experimental results show that the proposed method can obtain good estimation results in the simulated scenes in the laboratory and on parking or moving buses. This approach is effective to illumination changes, shadows and occlusion of passengers.展开更多
Object detection is the most fundamental but challenging issues in the field of computer vision.Object detection identifies the presence of various individual objects in an image.Great success is attained for object d...Object detection is the most fundamental but challenging issues in the field of computer vision.Object detection identifies the presence of various individual objects in an image.Great success is attained for object detection/recognition problems in the controlled environment,but still,the problem remains unsolved in the uncontrolled places,particularly,when the objects are placed in arbitrary poses in an occluded and cluttered environment.In the last few years,a lots of efforts are made by researchers to resolve this issue,because of its wide range of applications in computer vision tasks,like content-enabled image retrieval,event or activity recognition,scene understanding,and so on.This review provides a detailed survey of 50 research papers presenting the object detection techniques,like machine learning-based techniques,gradient-based techniques,Fast Region-based Convolutional Neural Network(Fast R-CNN)detector,and the foreground-based techniques.Here,the machine learning-based approaches are classified into deep learning-based approaches,random forest,Support Vector Machine(SVM),and so on.Moreover,the challenges faced by the existing techniques are explained in the gaps and issues section.The analysis based on the classification,toolset,datasets utilized,published year,and the performance metrics are discussed.The future dimension of the research is based on the gaps and issues identified from the existing research works.展开更多
基金supported by Fund of National Science & Technology monumental projects under Grants No.61105015,NO.61401239,NO.2012-364-641-209
文摘Foreground detection is a fundamental step in visual surveillance.However,accurate foreground detection is still a challenging task especially in dynamic backgrounds.In this paper,we present a nonparametric approach to foreground detection in dynamic backgrounds.It uses a history of recently pixel values to estimate background model.Besides,the adaptive threshold and spatial coherence are introduced to enhance robustness against false detections.Experimental results indicate that our approach achieves better performance in dynamic backgrounds compared with several approaches.
基金National Natural Science Foundations of China(Nos.61374097,61601108)the Fundamental Research Funds for the Central Universities,China(No.N130423006)the Foundation of Northeastern University at Qinhuangdao,China(No.XNK201403)
文摘Motionless foreground objects are key targets in applications of home care monitoring and abandoned object detection, and pose a great challenge to foreground detection. Most algorithms incorporate the motionless foreground objects into their background models because they have to adapt to environmental changes. To overcome this challenge, a foreground detection method based on nonlinear independent component analysis （ICA） was proposed. Considering that each video frame was actually a nonlinear mixture of the background image and the foreground image, the nonlinear ICA was employed to accurately separate the independent components from each frame. Then, the entropy of grayscale image was calculated to classify which resulting independent component was the foreground image. The proposed nonlinear ICA model was trained offiine and this model was not updated online, so the method can cope with the motionless foreground objects. Experimental results demonstrate that, the method achieves remarkable results and outperforms several advanced methods in dealing with the motionless foreground objects.
文摘A multiscale foreground detection method was developed to segment moving objects from a sta- tionary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the difference map between an input image and its background and ends at a final contour. An adaptive algorithm was developed to calculate an appropriate energy threshold to control the contours to identify the foreground silhouettes. Experiments show that this method more successfully ignores the nega- tive influence of image noise to obtain an accurate foreground map than other foreground detection algo- rithms. Most shadow pixels are also eliminated by this method.
基金supported by Fund of National Science&Technology monumental projects under Grants NO.61401239,NO.2012-364-641-209
文摘In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object.In this paper,we present an approach to detecting the parameters of a moving ship in an inland river.This approach involves interactive calibration without a calibration reference.We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios,and we detect ship length,width,speed,and flow.We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90%for all parameters.
基金supported by the Development Foundation of Shanghai Municipal Commission of Science and Technology (Grant No.072112007)the Shanghai Leading Acdemic Discipline Project (Grant No.J50104)
文摘An estimation approach is proposed in this paper based on the binocular stereovision to collect the degree of crowdedness in public transports. The proposed method combines the disparity with frame differences to extract the foreground object. An adaptive window normalized cross correlation （NCC） matching and interpolated method is applied to get the sub-pixel image disparity value. Then, the foreground object is projected to the horizontal plane to eliminate the influence of the occlusion and perspective effect. Finally the degree of crowdedness is calculated from the area and the perimeter of the foreground objects. Experimental results show that the proposed method can obtain good estimation results in the simulated scenes in the laboratory and on parking or moving buses. This approach is effective to illumination changes, shadows and occlusion of passengers.
文摘Object detection is the most fundamental but challenging issues in the field of computer vision.Object detection identifies the presence of various individual objects in an image.Great success is attained for object detection/recognition problems in the controlled environment,but still,the problem remains unsolved in the uncontrolled places,particularly,when the objects are placed in arbitrary poses in an occluded and cluttered environment.In the last few years,a lots of efforts are made by researchers to resolve this issue,because of its wide range of applications in computer vision tasks,like content-enabled image retrieval,event or activity recognition,scene understanding,and so on.This review provides a detailed survey of 50 research papers presenting the object detection techniques,like machine learning-based techniques,gradient-based techniques,Fast Region-based Convolutional Neural Network(Fast R-CNN)detector,and the foreground-based techniques.Here,the machine learning-based approaches are classified into deep learning-based approaches,random forest,Support Vector Machine(SVM),and so on.Moreover,the challenges faced by the existing techniques are explained in the gaps and issues section.The analysis based on the classification,toolset,datasets utilized,published year,and the performance metrics are discussed.The future dimension of the research is based on the gaps and issues identified from the existing research works.
