Anomaly detection in surveillance videos based on H265 and deep learning

doi:10.19101/ijatee.2021.875907

Cited by 6 publications

(22 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second, video-level labels, which mean we only know that each movie has an abnormality but not which exact part is abnormal. This maybe leads to overfitting [2], [22]. In this work, we picked the video with a length less than or equal to 2 min, depending on this condition, we had 1324 videos in total divided as follows: 1116 videos for the training purpose (90% for training and 10% for validation), and the remainder, which was 208, was used for the testing purpose.…”

Section: Input Database Ucf-crimementioning

confidence: 99%

“…Although deep structures require more time to train, they perform better than straightforward artificial neural networks (ANNs). However, strategies like transfer learning and GPU computing can shorten the training period [1], [2], [25].…”

Section: Machine Learning (Ml) and Deep Learning (Dl)mentioning

confidence: 99%

“…Despite the processing of the spatial data using CNN being good, for handling the sequential data RNN is better. This is due to RNN utilizing state variables to save the past data and use them beside the present inputs to determine the present outputs [2], [26]. Most of the time, CNN is used in image processing.…”

Section: Machine Learning (Ml) and Deep Learning (Dl)mentioning

confidence: 99%

“…Video surveillance systems (VSS) are frequently utilized to increase public safety in various settings, including malls, hospitals, banks, markets, intelligent cities, educational institutions, and roadways [1]. The main goal of security applications is typically the accuracy and speed of video anomaly identification [2]. For the sake of public safety, numerous surveillance cameras have recently been put in numerous areas throughout the world [3].…”

Section: Introductionmentioning

confidence: 99%

“…To detect video anomalies, Cao et al [18] suggested taking into consideration the spatial-temporal relationships between video parts. Abbas and Al-Ani [2] suggest compressing each video using high-efficiency video coding (H265) before feeding the video into the anomaly detection systems. An algorithm for reducing the size of the extracted features has been suggested by Abbas and Al-Ani before anomaly identification [19].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An adaptive algorithm based on principal component analysis-deep learning for anomalous events detection

Abbas

Al-Ani

2022

IJEECS

View full text Add to dashboard Cite

One of the most often used applications of human activity detection is anomaly detection, which is covered in this paper. Providing security for a person is a key issue in every community nowadays because of the constantly expanding activities that pose danger, from planned violence to harm caused by an accident. Existing classical closed-circuit television considered is insufficient since it needs a person to stay awake and constantly monitor the cameras, which is expensive. In addition, a person's attention decreases after a certain time. For these reasons, the development of an automated security system that can identify suspicious activities in real-time and quickly aid victims is required. Because identifying activity must be with high accuracy, and in the shortest possible time. We adopt an adaptive algorithm based on the combination of machine learning (ML), principal component analysis (PCA) and deep learning (DL). The UCF-crime dataset was used for the experimentation in this work. Where the area under the curve (AUC) with the proposed approach was equal to 94.21% while the detection accuracy was equal to 88.46% on the test set database. The suggested system has demonstrated its robustness and accomplishment of the best accuracy when compared with earlier designed systems.

show abstract

Section: Input Database Ucf-crimementioning

confidence: 99%