CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction

Mandal, Murari; Saxena, Prafulla; Vipparthi, Santosh Kumar; Murala, Subrahmanyam

doi:10.1109/icpr.2018.8545504

Cited by 18 publications

(15 citation statements)

References 24 publications

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“…Our post-processing step is minimal like other state-ofthe-art methods [11], [35]: we apply a median blur filter and binary morphological closing on S i to eliminate saltand-pepper noise. The final binary segmented result is called S P ostP roc i .…”

Section: B Background Generationmentioning

confidence: 99%

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Dynamic Background Subtraction by Generative Neural Networks

Bahri¹,

Ray²

2022

Preprint

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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.

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Section: B Background Generationmentioning

confidence: 99%

“…The supervised methods and ensemble method IUTIS, that combines several algorithms, [15] are not considered. In addition, algorithm [35], that was specifically proposed for dynamic background subtraction, is also considered.…”

Section: Comparisonmentioning

confidence: 99%

Dynamic Background Subtraction by Generative Neural Networks

Bahri¹,

Ray²

2022

Preprint

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“…Advancement in deep learning algorithms and the availability of largescale labeled datasets has fueled the progress in important applications in several domains. Some of the low-level tasks in computer vision include image classification [10,20,21,55], object detection [31-33, 36, 53, 73], semantic segmentation [7,19], video object segmentation [44,51,69], motion detection [1,38,39,41,48,62,71] and visual tracking [2,15,61]. Although many challenging applications are presented to the researchers in UAV based computer vision community.…”

Section: Mor-uav Dataset 21 Comparison With Existing Uav Datasetsmentioning

confidence: 99%

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

Mandal

Kumar

Vipparthi

2020

Proceedings of the 28th ACM International Conference on Multimedia

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Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new frontier of computer vision that requires automated analysis of aerial images/videos. However, the existing UAV datasets primarily focus on object detection. An object detector does not differentiate between the moving and non-moving objects. Given a real-time UAV video stream, how can we both localize and classify the moving objects, i.e. perform moving object recognition (MOR)? The MOR is one of the essential tasks to support various UAV vision-based applications including aerial surveillance, search and rescue, event recognition, urban and rural scene understanding.To the best of our knowledge, no labeled dataset is available for MOR evaluation in UAV videos. Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes for moving objects which requires less computational resources than producing pixel-level estimates. We annotate 89,783 moving object instances collected from 30 UAV videos, consisting of 10,948 frames in various scenarios such as weather conditions, occlusion, changing flying altitude and multiple camera views. We assigned the labels for two categories of vehicles (car and heavy vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present 16 baseline results based on the proposed framework over the MOR-UAV dataset through quantitative and qualitative experiments. We also analyze the motion-salient regions in the network through multiple layer visualizations. The MOR-UAVNet works online at inference as it requires only few past frames. Moreover, it doesnâĂŹt require predefined target initialization from user. Experiments also demonstrate that the MOR-UAV dataset is quite challenging.

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“…7 shows example video frames of CDnet datasets. There are a lot of works, for instance [115]- [120], using CDnet 2014 dataset in the validation of background subtraction algorithms.…”

Section: ) Cdnet Datasetmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction

Cited by 18 publications

References 24 publications

Dynamic Background Subtraction by Generative Neural Networks

Dynamic Background Subtraction by Generative Neural Networks

MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos

A Comprehensive Survey of Video Datasets for Background Subtraction

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