Gaussian Mixture Model (GMM) Based Dynamic Object Detection and Tracking

Anand, Vishnu; Pushp, Durgakant; Raj, Rishin; Das, Kaushik

doi:10.1109/icuas.2019.8797927

Cited by 8 publications

(1 citation statement)

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“…GMM is a statistical model, which describes spatial distribution and the properties of the data in the parameter space. GMM includes a parametric probability density function, which is comprised of numerous Gaussian component functions for detecting vehicles 9 Wireless Communications and Mobile Computing from the images [30], that is mathematically defined in equation (6). Sample preprocessed and vehicle-detected images are graphically represented in Figure 4:…”

Section: Imagementioning

confidence: 99%

Moving Vehicle Detection and Classification Using Gaussian Mixture Model and Ensemble Deep Learning Technique

Jagannathan¹,

Sujatha

Frnda

et al. 2021

Wireless Communications and Mobile Computing

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In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID-19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real-time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.

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Section: Imagementioning

confidence: 99%