PERFORMANCE ANALYSIS OF OBJECT DETECTION ALGORITHMS BASED ON TRANSFORM AND STATISTICAL TEXTURE CLASSIFICATION METHODS.

This paper presents the performance analysis of object detection algorithms using phase and statistical texture features. Robust metrics like accuracy and precision are used to perform the evaluation. Feature extraction is one of the prime requirements to represent an object. This paper provides a comprehensive and broad framework to phase feature and statistical feature extraction techniques and compares their performance in terms of accuracy and precision. In phase-based approach of object detection group of phase features are used to model the background. Whereas we represented the statistics of object by calculating GLCM features of the block images in statistical feature extraction methods. Support Vector machine algorithm is used to classify object. This method is developed to achieve higher accuracy and speed. Performance analysis of these two methods shows that phase feature is robust to illumination variations. Also, the obtained accuracy is good, but the computational complexity is high. Whereas co-occurrence matrix based statistical approach of object detection a good tradeoff between speed and accuracy. [ABSTRACT FROM AUTHOR]