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Anomaly detection in video frames: hybrid gain optimized Kalman filter

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Abstract

Even though there are exist numerous studies that aim at anomaly detection under object tracking, such techniques are not very practicable in crowded areas since motions are not completely enclosed in a single frame. This research work concerns on introducing a new model that detects anomalies in video frames, which is performed under three stages such as, (i) motion estimation (ii) object tracking and (iii) Anomaly detection. At first, motion detection is done that intends to discover the movements of objects in the frames. Consequently, object tracking (second phase) is performed to track the moving objects by means of Extended Kalman Filter (EKF). After the tracking of object, anomaly detection (third phase) is carried out, which detects the presence of anomalies in the video. Moreover, it is very important to track the objects more precisely for identifying the presence of anomalies in the video scene. For attaining the precise tracking, this paper intends to tune the Kalman gain optimally, thus making the anomaly detection more precise. For the achievement of optimal gain, this paper introduces a new algorithm, termed as Wolf Updated-Whale Optimization Algorithm model (WU-WOA). At last, the effectiveness of adopted scheme is confirmed by evaluating over existing schemes. The precision of the adopted model is 54.76%, 54.76%, 58.54%, and 54.76% better than FF, PSO, GWO, and WOA methods.

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Data availability

No new data were generated or analysed in support of this research.

Abbreviations

CAE:

Convolutional Auto Encoder

GWO:

Grey Wolf Optimization

WOA:

Whale Optimization Algorithm

WU-WOA:

Wolf Updated-WOA model

EKF:

Extended Kalman Filter

GMM:

Gaussian mixture model

CNN:

Convolutional Neural Network

OCELM:

One-Class Extreme Learning Machine

LSHF:

Locality Sensitive Hashing Filters

3D-STAE:

3D Spatiotemporal autoencoder

FCNNs:

Fully CNN

OCSVM:

One Class Support Vector Machine

LR:

Low Resolution

MAD:

Mean Absolute Difference

NPV:

Net Present Value

MCC:

Matthews Correlation Coefficient

FPR:

False Positive Rate

FF:

FireFly

FDR:

False Discovery Rate

PSO:

Particle Swarm Optimization

FNR:

False Negative Rate

SCNN:

Siamese Convolutional Neural Network

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Authors and Affiliations

  1. Electronics Engineering, JC Bose University of Science and Technology YMCA, Faridabad, India

    Neetu Gupta & Gunjan Sardana

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  1. Neetu Gupta
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Gupta, N., Sardana, G. Anomaly detection in video frames: hybrid gain optimized Kalman filter. Multimed Tools Appl 82, 33961–33982 (2023). https://doi.org/10.1007/s11042-023-14827-x

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