  In the era of modern technology, object detection using the Gray Level
Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object
recognition processes. It finds applications in real-time scenarios such as
security surveillance and autonomous vehicle navigation, among others.
Computational efficiency becomes a critical factor in achieving real-time
object detection. Hence, there is a need for a detection model with low
complexity and satisfactory accuracy. This research aims to enhance
computational efficiency by selecting appropriate features within the GLCM
framework. Two classification models, namely K-Nearest Neighbours (K-NN) and
Support Vector Machine (SVM), were employed, with the results indicating that
K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational
complexity. Specifically, K-NN, when utilizing a combination of Correlation,
Energy, and Homogeneity features, achieves a 100% accuracy rate with low
complexity. Moreover, when using a combination of Energy and Homogeneity
features, K-NN attains an almost perfect accuracy level of 99.9889%, while
maintaining low complexity. On the other hand, despite SVM achieving 100%
accuracy in certain feature combinations, its high or very high complexity can
pose challenges, particularly in real-time applications. Therefore, based on
the trade-off between accuracy and complexity, the K-NN model with a
combination of Correlation, Energy, and Homogeneity features emerges as a more
suitable choice for real-time applications that demand high accuracy and low
complexity. This research provides valuable insights for optimizing object
detection in various applications requiring both high accuracy and rapid
responsiveness.
