CAR-DCGAN: A Deep Convolutional Generative Adversarial Network for Compression Artifact Removal in Video Surveillance Systems

Aqqa, Miloud; Shah, Shishir K.

doi:10.5220/0010312304550464

Cited by 3 publications

(2 citation statements)

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“…Motion that diverges from the anticipated or standard route Speed Variation ([106]) Uncommon alterations in the velocity of moving objects or persons Crowd-based UMCD [21] 20 Not Given Not Given Motion-based UCSD Ped1 [120] 70 34 36 Crowd-based UCSD Ped2 [120] 28 16 12 Crowd-based Subway [8] 1 --Point-based Avenue [135] 37 16 21 Point-based Street Scene [167] 81 46 35 Object-based Behave [31] 4 2 2 Behavioral UCF Crime [194] 1900 1610 290 Motion-based ShanghaiTech [130] 437 330 107 Motion-based standing of their practical implications.…”

Section: Directional Deviation ([114])mentioning

confidence: 99%

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A Comprehensive Survey of Machine Learning Methods for Surveillance Videos Anomaly Detection

Choudhry,

Abawajy,

Huda

et al. 2023

IEEE Access

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Video Surveillance Systems (VSSs) are used in a wide range of applications including public safety and perimeter security. They are deployed in places such as markets, hospitals, schools, banks, shopping malls, offices, and smart cities. VSSs generate a massive amount of surveillance data, and significant research has been published on the use of machine learning algorithms to handle surveillance data. In this paper, we present an extensive overview and a thorough analysis of cutting-edge learning methods used in VSSs. Existing surveys on learning approaches in video surveillance have some drawbacks, such as a lack of in-depth analysis of the learning algorithms, omission of certain methodologies, insufficient critical evaluation, and absence of recent learning algorithms. To fill these gaps, this survey provides a thorough examination of the most recent learning algorithms for anomaly detection. A critical assessment of the algorithms including their strengths, weaknesses, and applicability as well as tailored classifications of anomaly types for different domains are provided. Our study also offers insights into the future development of learning techniques in VSS, positioning itself as a valuable resource for both researchers and practitioners in the field. Finally, we share our thoughts on what we learned and how it can help with new developments in the future.

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Section: Directional Deviation ([114])mentioning

confidence: 99%

“…A Generative Model ( [218,23,239,16]) is designed to learn the joint probability distribution and utilizes Bayes Theorem to predict conditional probabilities. Generative classifiers, such as Naive Bayes, Bayesian Networks, Markov Random Fields, and Hidden Markov Models (HMM), exemplify this approach.…”

Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%