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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

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Detection of Moving Object in Dynamic Visual Sequences Based on Partial Least Squares Classifier.

Shyamala Balakumar¹, Selvaperumal Sundaramoorthy², Ramasubramanian Bhoopalan³

¹Department of ECE, Mohamed Sathak Engineering College, Kilakarai, Ramanathapuram, India. shyamalabalakumar@yahoo.com.

Journal of Medical Systems

|July 3, 2019

Summary

This study enhances moving object detection in video sequences using Local Binary Pattern and grey level co-efficient features. The Partial Least Squares (PLS) classifier achieved the highest accuracy, though with increased computation time.

Keywords:

Adaptive histogram equalization Local binary pattern PLS PNN SVM

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Area of Science:

Computer Vision
Machine Learning
Pattern Recognition

Background:

Moving object detection is crucial for object tracking in video analysis.
Accurate detection and classification of objects in video sequences remain a challenge.

Purpose of the Study:

To detect and classify moving objects in video sequences.
To evaluate and compare the performance of different classification algorithms for this task.

Main Methods:

Video sequences were collected from public datasets and individual frames were extracted.
A novel background subtraction method was applied after frame pre-processing.
Features were extracted using Local Binary Pattern (LBP) and grey level co-efficient.
Classifiers including Support Vector Machine (SVM), Partial Least Squares (PLS), and Probabilistic Neural Network (PNN) were employed.

Main Results:

The Partial Least Squares (PLS) classifier demonstrated superior classification accuracy compared to SVM and PNN.
The PLS classifier required a longer computation time.

Conclusions:

Feature extraction using LBP and grey level co-efficient is effective for object detection.
PLS offers high accuracy for moving object classification in videos, balancing performance and computational cost.