Automatic moving object and background separation

Neri, Alessandro; Colonnese, Stefania; Russo, Giuseppe; Talone, P.

doi:10.1016/s0165-1684(98)00007-3

Cited by 197 publications

(98 citation statements)

References 13 publications

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“…Moreover, the moving objects in image sequences are obeyed high-order statistical distributions. Therefore, the moving objects can be subsequently obtained by a high-order statistical operator to filter the differential images in certain areas [11]. Moreover, Canny feature points can be extracted from image sequences, and the feature images are differentiated, so the moving targets are extracted by the characteristics of the feature points [12].…”

Section: Methodsmentioning

confidence: 99%

“…At present, mainstream target detection methods are divided into two classes: background difference methods and inter-frame difference methods [2]. The background difference method uses various algorithms to obtain the background image, and then subtracts the background The inter-frame difference method is another method of target detection that uses the difference between two or more frames to obtain the shape, position and other information about the moving object [9][10][11]. Based on the continuous difference between two or more frames to update the background information, an entire background model is obtained to extract moving objects [10].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Moving Ships in Sequences of Remote Sensing Images

Yao

Chang

Cheng

et al. 2017

IJGI

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High-speed agile remote sensing satellites have the ability to capture multiple sequences of images. However, the frame rate is lower and the baseline between each image is much longer than normal image sequences. As a result, the edges and shadows in each image in the sequence vary considerably. Therefore, more requirements are placed on the target detection algorithm. Aiming at the characteristics of multi-view image sequences, we propose an approach to detect moving ships on the water surface. Based on marker controlled watershed segmentation, we use the extracted foreground and background images to segment moving ships, and we obtain the complete shape and texture information of the ships. The inter-frame difference algorithm is applied to extract the foreground object information, while Otsu's algorithm is used to extract the image background. The foreground and background information is fused to solve the problem of interference with object detection caused by long imaging baseline. The experimental results show that the proposed method is effective for moving ship detection.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Moving Ships in Sequences of Remote Sensing Images

Yao

Chang

Cheng

et al. 2017

IJGI

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“…For VO extraction using the representation and localization technique, a reference model representing the object must first be created which can be done either in an automatic [2][3][4][5][6][7] or semiautomatic fashion [8][9][10]. A variety of models has been proposed including: 2D mesh [30,31], binary model [32], color histogram [29], deformable templates [33], corners and lines [34], active contour [9], 2D regions [35], etc.…”

Section: Related Workmentioning

confidence: 99%

“…For this purpose a great deal of approaches have been proposed [2][3][4][5][6][7][8][9][10], which provide satisfactory results for extracting VOs of homogeneous motion characteristics. Unfortunately, dealing with VOs with abrupt motions or occlusions remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

Applying the multi-category learning to multiple video object extraction

Liu¹,

Zheng

Shen

2008

Pattern Recognition

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“…To move ahead, just by counting of vehicles and their classification we can reduce the deceptive activities in many areas such as toll collection, parking area. These motion based detection and classification methods comprises interframe difference method [25] optical flow estimation [13] Gaussian scale mixture model methods [46] [28] and background subtraction methods [26] [18] [9]. Out of all the above mentioned methods background subtraction is the most common techniques used in vehicle detection.…”

Section: Introductionmentioning

confidence: 99%

Multiple Kernel based KNN Classifiers for Vehicle Classification

Mishra

Banerjee²

2013

IJCA

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The problem of vehicle classification has been addressed in this correspondence. Vehicle classification is a difficult task due to near similarity among various types vehicle features. Spectral properties of the image and near similarity between the front side of different vehicles makes the generalization process even more difficult. Here a multiple kernel based k-nearest neighbor classifier has been designed to improve the classification accuracy. After extracting the frames from the traffic video, vehicles are detected using background subtraction method. Then a wavelet and interest point based feature extraction step is carried out for each detected vehicle. Final classification is carried out using the newly proposed multiple kernel based k-nearest neighbor( KNN) algorithm. Experiments on several real time data-sets establish the higher accuracy of the proposed method in comparison to three well-known state of the art classification techniques.

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Automatic moving object and background separation

Cited by 197 publications

References 13 publications

Detection of Moving Ships in Sequences of Remote Sensing Images

Detection of Moving Ships in Sequences of Remote Sensing Images

Applying the multi-category learning to multiple video object extraction

Multiple Kernel based KNN Classifiers for Vehicle Classification

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