Recognition of Moving Objects in Videos of Moving Camera with Harris Attributes

Nozari, Ali; Hoseini, Seyed Reza

doi:10.1109/micai.2015.13

Cited by 3 publications

(1 citation statement)

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“…The background compensation method estimates the camera's motion based on optical flow, block features [24], and point features, such as Harris [25], the scale-invariant feature transform (SIFT) [26], and oriented fast and rotated brief (ORB) [27]. For example, Adel Hafiane et al [28] extracted a corner-based feature block to compensate for the moving background and their approach obtained good performance with videos captured by a camera on a UAV.…”

Section: Moving Object Detection Methodsmentioning

confidence: 99%

Moving Object Detection Based on Background Compensation and Deep Learning

Zhu

Wang

et al. 2020

Symmetry

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Detecting moving objects in a video sequence is an important problem in many vision-based applications. In particular, detecting moving objects when the camera is moving is a difficult problem. In this study, we propose a symmetric method for detecting moving objects in the presence of a dynamic background. First, a background compensation method is used to detect the proposed region of motion. Next, in order to accurately locate the moving objects, we propose a convolutional neural network-based method called YOLOv3-SOD for detecting all objects in the image, which is lightweight and specifically designed for small objects. Finally, the moving objects are determined by fusing the results obtained by motion detection and object detection. Missed detections are recalled according to the temporal and spatial information in adjacent frames. A dataset is not currently available specifically for moving object detection and recognition, and thus, we have released the MDR105 dataset comprising three classes with 105 videos. Our experiments demonstrated that the proposed algorithm can accurately detect moving objects in various scenarios with good overall performance.

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Section: Moving Object Detection Methodsmentioning

confidence: 99%