An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities

Maqsood, Muazzam; Yasmin, Sadaf; Gillani, Saira; Bukhari, Maryam; Rho, Seungmin; Yeo, Sang-Soo

doi:10.1007/s11704-022-2050-4

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…Recent advances have been made in image analysis, speech recognition, and game strategy. A deep neural network can perform as well as or better than a human in some situations [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Ahmed

Lin

2023

J Supercomput

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This paper proposes a deep learning model that is robust and capable of handling highly uncertain inputs. The model is divided into three phases: creating a dataset, creating a neural network based on the dataset, and retraining the neural network to handle unpredictable inputs. The model utilizes entropy values and a non-dominant sorting algorithm to identify the candidate with the highest entropy value from the dataset. This is followed by merging the training set with adversarial samples, where a mini-batch of the merged dataset is used to update the dense network parameters. This method can improve the performance of machine learning models, categorization of radiographic images, risk of misdiagnosis in medical imaging, and accuracy of medical diagnoses. To evaluate the efficacy of the proposed model, two datasets, MNIST and COVID, were used with pixel values and without transfer learning. The results showed an increase of accuracy from 0.85 to 0.88 for MNIST and from 0.83 to 0.85 for COVID, which suggests that the model successfully classified images from both datasets without using transfer learning techniques.

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“…Recent advances have been made in image analysis, speech recognition, and game strategy. A deep neural network can perform as well as or better than a human in some situations [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Ahmed

Lin

2023

J Supercomput

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show abstract

“…To achieve improved object detection results, a study of Real-Time abnormal object detection [24] shows how to train and implement the abnormal object detection model into smart cities with reaching about 90 percent accuracy [25] [26]. Another paper published in 2023, which discussed person re-identification using deep learningassisted methods [27] focuses on learning spatial and channel attention between different views of the same object to have a machine learning-related solution for better re-identification scores of 24.6 percent and 54.8 percent.…”

Section: Related Workmentioning

confidence: 99%

Real-World Community-in-the-Loop Smart Video Surveillance -- A Case Study at a Community College

Yao¹,

Ardabili²,

Pazho³

et al. 2023

Preprint

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Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming has made deploying these systems quite challenging.This paper presents a case study for designing and deploying smart video surveillance systems based on a real-world testbed at a community college. We primarily focus on a smart camerabased system that can identify suspicious/abnormal activities and alert the stakeholders and residents immediately. The paper highlights and addresses different algorithmic and system design challenges to guarantee real-time high-accuracy video analytics processing in the testbed. It also presents an example of cloud system infrastructure and a mobile application for realtime notification to keep students, faculty/staff, and responsible security personnel in the loop. At the same time, it covers the design decision to maintain communities' privacy and ethical requirements as well as hardware configuration and setups.We evaluate the system's performance using throughput and end-to-end latency. The experiment results show that, on average, our system's end-to-end latency to notify the end users in case of detecting suspicious objects is 5.3, 5.78, and 11.11 seconds when running 1, 4, and 8 cameras, respectively. On the other hand, in case of detecting anomalous behaviors, the system could notify the end users with 7.3, 7.63, and 20.78 seconds average latency. These results demonstrate that the system effectively detects and notifies abnormal behaviors and suspicious objects to the end users within a reasonable period. The system can run eight cameras simultaneously at a 32.41 Frame Per Second (FPS) rate.

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Efficient Multi-modal Human-Centric Contrastive Pre-training with a Pseudo Body-Structured Prior

Meng,

Cheng,

Wang

et al. 2024

Lecture Notes in Computer Science

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An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities

Cited by 9 publications

References 46 publications

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

RETRACTED ARTICLE: Robust adversarial uncertainty quantification for deep learning fine-tuning

Real-World Community-in-the-Loop Smart Video Surveillance -- A Case Study at a Community College

Efficient Multi-modal Human-Centric Contrastive Pre-training with a Pseudo Body-Structured Prior

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