An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection

Dvořák, R.; Drahanský, Martin

doi:10.1109/icig.2007.153

Cited by 167 publications

(73 citation statements)

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“…In this study, two classical algorithms (frame difference [9][10][11], optical flow [7], [8]) are also implemented in accordance with the situation. In Tab.…”

Section: Resultsmentioning

confidence: 99%

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Automatic Detection of Aerial Vehicle in Cloudy Environment by Using Wavelet Enhancement Technique

Pakfiliz¹

2017

RADIOENGINEERING

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“…In this study, two classical algorithms (frame difference [9][10][11], optical flow [7], [8]) are also implemented in accordance with the situation. In Tab.…”

Section: Resultsmentioning

confidence: 99%

“…Frame difference method is simple and easy to implement, but the results are not as accurate as the results of the other methods. This is because the changes taking place in the background brightness cause misjudgment [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Aerial Vehicle in Cloudy Environment by Using Wavelet Enhancement Technique

Pakfiliz¹

2017

RADIOENGINEERING

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“…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]. Although these algorithms are more adaptable to real environments than the background difference algorithms, they are influenced by the displacement of the moving target.…”

Section: Methodsmentioning

confidence: 99%

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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“…Machine learning methods treat tracking as a binary classification problem with a local search algorithm, which estimates the decision boundary between an object image patch and the background. Whereas efficient feature extraction techniques have been proposed for visual tracking, a large number of samples often exist from which features need to be extracted for classification [15]. According to the properties of these methods and our application scenarios, this study presents an improved algorithm that combines frame difference and machine learning with faster region-based convolutional neural network (R-CNN).…”

Section: Principle Of the Proposed Methodsmentioning

confidence: 99%

“…To detect a moving object in a surveillance video captured with an immobile camera, the frame difference method is the simplest technique to employ owing to its high detection speed and ease of implementation on hardware [15].…”

Section: Frame Difference Methodsmentioning

confidence: 99%

Aerial Target Tracking Algorithm Based on Faster R-CNN Combined with Frame Differencing

et al. 2017

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Abstract:We propose a robust approach to detecting and tracking moving objects for a naval unmanned aircraft system (UAS) landing on an aircraft carrier. The frame difference algorithm follows a simple principle to achieve real-time tracking, whereas Faster Region-Convolutional Neural Network (R-CNN) performs highly precise detection and tracking characteristics. We thus combine Faster R-CNN with the frame difference method, which is demonstrated to exhibit robust and real-time detection and tracking performance. In our UAS landing experiments, two cameras placed on both sides of the runway are used to capture the moving UAS. When the UAS is captured, the joint algorithm uses frame difference to detect the moving target (UAS). As soon as the Faster R-CNN algorithm accurately detects the UAS, the detection priority is given to Faster R-CNN. In this manner, we also perform motion segmentation and object detection in the presence of changes in the environment, such as illumination variation or "walking persons". By combining the 2 algorithms we can accurately detect and track objects with a tracking accuracy rate of up to 99% and a frame per second of up to 40 Hz. Thus, a solid foundation is laid for subsequent landing guidance.

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An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection

Cited by 167 publications

References 1 publication

Automatic Detection of Aerial Vehicle in Cloudy Environment by Using Wavelet Enhancement Technique

Automatic Detection of Aerial Vehicle in Cloudy Environment by Using Wavelet Enhancement Technique

Detection of Moving Ships in Sequences of Remote Sensing Images

Aerial Target Tracking Algorithm Based on Faster R-CNN Combined with Frame Differencing

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