Modeling Representation of Videos for Anomaly Detection using Deep Learning: A Review

Chong, Yong Shean; Tay, Yong Haur

doi:10.48550/arxiv.1505.00523

Cited by 4 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A short review on the subject of video anomaly detection is provided here [16]. To the best of our knowledge, there has not been a systematic study of deep architectures for video anomaly detection, which is characterized by abnormal appearance and motion features, that occur rarely.…”

Section: Context Of the Reviewmentioning

confidence: 99%

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

Kiran

Thomas²,

Parakkal³

2018

J. Imaging

396

153

View full text Add to dashboard Cite

Abstract:Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.

show abstract

Section: Context Of the Reviewmentioning

confidence: 99%

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

Kiran

Thomas²,

Parakkal³

2018

J. Imaging

396

153

View full text Add to dashboard Cite

show abstract

“…the analysis of movies for abnormalities using deep learning. It is difficult to find anomalies in videos [22]. The clarity of the video and the range of changes that might occur inside the film, including human motions [23] and environmental variables, are what make this work challenging.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Crowd behavior Analysis and Forecasting

Bhardwaj

Dwivedi

Pandey³

et al. 2023

IJSRCSEIT

View full text Add to dashboard Cite

In many places today, the world's overcrowding causes crowded conditions. Analysis of crowd activity is a developing field of study. It is common knowledge that mob activity can forecast what might happen during an event. Crowd management could be very effective if situations like riots, mass lynchings, traffic jams, accidents, stampedes, etc. could be predicted beforehand. In this paper, we propose a new multicolumn convolutional neural network (MCNN) based technique for predicting mob behavior. The features of the incoming image are first analyzed and extracted. The approximated number of the gathering is then established, and image cropping is completed. For each area of the image, low level characteristics are retrieved. The objects in the picture are then created as density images. Using our method, the gathered characteristics and their object density maps are then linearly mapped. At last, we forecast and quantify the population using the MCNN algorithm. For the ShanghaiTech dataset, we have evaluated our method using actual data.

show abstract

“…Chung et al [9] have presented a review article that describes how to solve the problem of showing abnormal event videos. The concept of the conditioned finite Boltzmann machine and the independent component analysis has been used to extract better features (as a common easy method).…”

Section: Related Workmentioning

confidence: 99%

“…Samples that comply with the rules are classified as normal behaviors, and samples that do not comply with the rules are classified as abnormal. Online dictionary update and flexible encryption [9,18,19] are two techniques primarily used in the rule-based approach. The third strategy is unsupervised, which does not require both usual and unusual cases as training data.…”

Section: Related Workmentioning

confidence: 99%

Unusual Human Behavior Detection System in Real-Time Video Systems

Bu,

Chen,

Duan

et al. 2023

IJACSA

View full text Add to dashboard Cite

Abnormal behavior detection, in terms of importance, has become a necessity in real-time visual systems. The main problem is the ambiguity in the difference between the characteristics of abnormal and normal behavior, which its definition is usually different according to the previous context of images. In this research, three approaches are used. In the first approach, a standard Convolutional Automatic Encoder (CAE) is used. After evaluation, it was found that the standard CAE problem is that it does not take into account the temporal aspect of the image frames sequence. The second method involves automatic encoding to learn the dataset's spatio-temporal structures. In the third approach, the complex LSTM cells are used for further improvement. The outcomes of the test display that the proposed methods have better performance compared to many of the previous conventional methods, and their efficiency in identifying abnormal behavior is very competitive compared to previous methods.

show abstract

Modeling Representation of Videos for Anomaly Detection using Deep Learning: A Review

Cited by 4 publications

References 0 publications

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

An Overview of Deep Learning Based Methods for Unsupervised and Semi-Supervised Anomaly Detection in Videos

Machine Learning-Based Crowd behavior Analysis and Forecasting

Unusual Human Behavior Detection System in Real-Time Video Systems

Contact Info

Product

Resources

About