Robust Cross-Scene Foreground Segmentation in Surveillance Video

Liang, Dong; Wei, Zongqi; Sun, Han; Zhou, Huiyu

doi:10.1109/icme51207.2021.9428086

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…In terms of model sharing, some methods have published their model files and training codes on Github, such as FgSegNets [ 44 , 45 ], BSUV [ 46 , 47 ], HOFAM [ 48 ], MUNet [ 49 ], GraphMOS [ 50 ], etc. The openness of Github allows anyone to access and use these models and codes.…”

Section: Related Workmentioning

confidence: 99%

“…After downloading the data, the model can be trained in a centralized manner on a local server or a third-party server, or it can be trained in a distributed manner by multiple clients. In terms of model sharing, some methods have published their model files and training codes on Github, such as FgSegNets [44,45], BSUV [46,47], HOFAM [48], MUNet [49], GraphMOS [50], etc. The openness of Github allows anyone to access and use these models and codes.…”

Section: Deep Model Research For Motion Object Segmentation and Its T...mentioning

confidence: 99%

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A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation

Jiang

et al. 2023

Sensors

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Model evaluation is critical in deep learning. However, the traditional model evaluation approach is susceptible to issues of untrustworthiness, including insecure data and model sharing, insecure model training, incorrect model evaluation, centralized model evaluation, and evaluation results that can be tampered easily. To minimize these untrustworthiness issues, this paper proposes a blockchain-based model evaluation framework. The framework consists of an access control layer, a storage layer, a model training layer, and a model evaluation layer. The access control layer facilitates secure resource sharing. To achieve fine-grained and flexible access control, an attribute-based access control model combining the idea of a role-based access control model is adopted. A smart contract is designed to manage the access control policies stored in the blockchain ledger. The storage layer ensures efficient and secure storage of resources. Resource files are stored in the IPFS, with the encrypted results of their index addresses recorded in the blockchain ledger. Another smart contract is designed to achieve decentralized and efficient management of resource records. The model training layer performs training on users’ servers, and, to ensure security, the training data must have records in the blockchain. The model evaluation layer utilizes the recorded data to evaluate the recorded models. A method in the smart contract of the storage layer is designed to enable evaluation, with scores automatically uploaded as a resource attribute. The proposed framework is applied to deep learning-based motion object segmentation, demonstrating its key functionalities. Furthermore, we validated the storage strategy adopted by the framework, and the trustworthiness of the framework is also analyzed.

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Section: Related Workmentioning

confidence: 99%

Section: Deep Model Research For Motion Object Segmentation and Its T...mentioning

confidence: 99%

A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation

Jiang

et al. 2023

Sensors

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Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects

Jiang

Zhu

et al. 2023

Mach. Intell. Res.

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Moving object detection via feature extraction and classification

2024

Open Computer Science

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Foreground segmentation (FS) plays a fundamental and important role in computer vision, but it remains a challenging task in dynamic backgrounds. The supervised method has achieved good results, but the generalization ability needs to be improved. To address this challenge and improve the performance of FS in dynamic scenarios, a simple yet effective method has been proposed that leverages superpixel features and a one-dimensional convolution neural network (1D-CNN) named SPF-CNN. SPF-CNN involves several steps. First, the coined Iterated Robust CUR (IRCUR) is utilized to obtain candidate foregrounds for an image sequence. Simultaneously, the image sequence is segmented using simple linear iterative clustering. Next, the proposed feature extraction approach is applied to the candidate matrix region corresponding to the superpixel block. Finally, the 1D-CNN is trained using the obtained superpixel features. Experimental results demonstrate the effectiveness of SPF-CNN, which also exhibits strong generalization capabilities. The average F1-score reaches 0.83.

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Robust Cross-Scene Foreground Segmentation in Surveillance Video

Cited by 3 publications

References 19 publications

A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation

A Blockchain-Based Trustworthy Model Evaluation Framework for Deep Learning and Its Application in Moving Object Segmentation

Deep Learning-based Moving Object Segmentation: Recent Progress and Research Prospects

Moving object detection via feature extraction and classification

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