Dynamic Background Subtraction by Generative Neural Networks
Background subtraction is a significant task in computer vision and an essential step for many real world applications. One of the challenges for background subtraction methods is dynamic background, which constitute stochastic movements in some parts of the background. In this paper, we have proposed a new background subtraction method, called DBSGen, which uses two generative neural networks, one for dynamic motion removal and another for background generation. At the end, the foreground moving objects are obtained by a pixel-wise distance threshold based on a dynamic entropy map. The proposed method has a unified framework that can be optimized in an end-to-end and unsupervised fashion. The performance of the method is evaluated over dynamic background sequences and it outperforms most of state-of-the-art methods. Our code is publicly available at https://github.com/FatemeBahri/DBSGen.
Code (1)
Similar Papers 제목 키워드 기반
Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models
Although it has been widely discussed in video surveillance, background subtraction is still an open problem in the context of complex scenarios, e.g., dynamic backgrounds, illumination variations, and indistinct foregro…
Unsupervised Deep Context Prediction for Background Foreground Separation
In many advanced video based applications background modeling is a pre-processing step to eliminate redundant data, for instance in tracking or video surveillance applications. Over the past years background subtraction …
Image Inpaintingobject-detectionObject DetectionPredictionCombining Background Subtraction Algorithms with Convolutional Neural Network
Accurate and fast extraction of foreground object is a key prerequisite for a wide range of computer vision applications such as object tracking and recognition. Thus, enormous background subtraction methods for foregrou…
DecoderObjectobject-detectionObject Detection+1Weakly Supervised Realtime Dynamic Background Subtraction
Background subtraction is a fundamental task in computer vision with numerous real-world applications, ranging from object tracking to video surveillance. Dynamic backgrounds poses a significant challenge here. Supervise…
Object TrackingReal-Time Semantic Background Subtraction
Semantic background subtraction SBS has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a semantic segmentation network. However, …
CPUGPUSemantic Segmentation