Anomaly detection using DBSCAN clustering technique for traffic video surveillance

Ranjith, R.; Athanesious, J. Joshan; Vaidehi, V.

doi:10.1109/icoac.2015.7562795

Cited by 23 publications

(12 citation statements)

References 23 publications

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“…The structure of roads is varying for different scene and it may unable to adopt and learn the patterns effectively. Ranjith et al [5] utilised the density‐based clustering technique which uses spatio‐temporal features for detecting anomaly events in TVS. In this method, parameters such as epsilon (eps) and a minimum number of points (minpts) play a vital role for efficient abnormal detection.…”

Section: Literature Surveymentioning

confidence: 99%

“…In summary, the key challenges identified from the above researches are: Detecting abnormal events using foreground detection is computationally high and also degrades the performance in terms of accuracy [11]. Vehicle with varying speed produces a different set of points [5]. Traffic scene contains all kinds of objects such as vehicles, humans, animals etc., which is very difficult to track based on object classification [19].…”

Section: Literature Surveymentioning

confidence: 99%

“…Several methodologies for anomaly detection in TVS [3] are mainly focusing on foreground modelling [5], adaptive learning [6], classification [7] and classifier [8], which have limitations such as effective clustering centre, imbalance data (normal and abnormal events) and different types of objects in the traffic scene. In this paper, super orientation optical flow (SOOF) features are proposed to detect anomalies in video traffic data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting abnormal events in traffic video surveillance using superorientation optical flow feature

Athanesious

Vasuhi

Vijayakumar

et al. 2020

IET Image Processing

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting abnormal events in traffic video surveillance using superorientation optical flow feature

Athanesious

Vasuhi

Vijayakumar

et al. 2020

IET Image Processing

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“…DBSCAN performs clustering by separating high and low-density regions within a data distribution. The DBSCAN algorithm is robust to noise and is highly scala-Journal of Intelligent Learning Systems and Applications ble [30]; it is used to detect anomalies in traffic video surveillance. Similarly, DBSCAN is used along with KDE in [31] to identify abnormal moving speed for coastal surveillance systems.…”

Section: Density Based Spatial Clustering Of Applications With Noisementioning

confidence: 99%

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Close¹,

Kashef²

2020

JILSA

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Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and real-time data transactions, the stock market has increased vulnerability to attacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipulations are not available for training. This paper discovers a competitive alternative to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Density-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

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“…Zhiguo et al [2] presented a novel anomaly detection framework based on Isolation Forest, in which they used the frame of sliding windows and also considered the concept of drift phenomenon. Ranjith et al [11] proposed an unsupervised anomaly detection model using the DBSCAN algorithm. They tried to find out anomalies from a traffic dataset, in which a trajectory is said to be an anomaly if it does not fit with the trained model.…”

Section: Related Workmentioning

confidence: 99%

An Effective Heart Disease Prediction Model based on Machine Learning Techniques

Ripan

Sarker

Furhad³

et al. 2020

Preprint

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This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

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Anomaly detection using DBSCAN clustering technique for traffic video surveillance

Cited by 23 publications

References 23 publications

Detecting abnormal events in traffic video surveillance using superorientation optical flow feature

Detecting abnormal events in traffic video surveillance using superorientation optical flow feature

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

An Effective Heart Disease Prediction Model based on Machine Learning Techniques

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