Human detection using iterative feature selection and logistic principal component analysis

We present a fast feature selection algorithm suitable for object detection applications where the image being tested must be scanned repeatedly to detected the object of interest at different locations and scales. The algorithm iteratively estimates the belongness probability of image pixels to foreground of the image. To prove the validity of the algorithm, we apply it to a human detection problem. The edge map is filtered using a feature selection algorithm. The filtered edge map is then projected onto an eigen space of human shapes to determine if the image contains a human. Since the edge maps are binary in nature, Logistic Principal Component Analysis is used to obtain the eigen human shape space. Experimental results illustrate the accuracy of the human detector.

Authors

• Wael Abd-Almageed
• Larry S. Davis

Keywords

• Humans
• Computer vision
• Logistics
• Principal component analysis
• Iterative algorithms
• Image edge detection
• Object detection
• Shape
• Testing
• Pixel
• Logistic Analysis
• Logistic Component
• Iterative Feature Selection
• Logistic Principal Component Analysis
• Object Of Interest
• Feature Selection Algorithm
• Shape Space
• Support Vector Machine
• Kernel Function
• Binary Data
• Training Images
• Projection Matrix
• Feature Points
• False Alarm Rate
• Human Imaging
• Still Images
• Compact Representation
• Observation Matrix
• Color Coordinates
• False Detection Rate
• Histogram Of Oriented Gradients
• Foreground Objects
• Non-human Objects
• Evolution Of The Probability