Abstract:
We, as human beings, are very good at extracting features that allow us to differentiate
between different types of objects in our surroundings. This is because we are well aware
of certain unique features that describe humans and help us separate humans from other
living and nonliving objects. In order for the machine to achieve the same task with
a high accuracy, we need to look for especially discriminant features that could help
achieve maximum separation in human and non-human classes. This project develops
an accurate human detection system that can detect human independent of variation
in scale, posture and level of illumination. A novel illumination invariant feature set
based on histogram of oriented gradients (HOG) and local phase quantization (LPQ)
is proposed. In order to evaluate the performance of the proposed human detection
system, it is trained on INRIA person dataset and tested on University of Central
Florida’s (UCF) sports action dataset and Change Detection (CDW 2014) dataset. The
area under the Receiver Operator Curve (ROC) of the detector turned out to be 0.781 for
UCF dataset and 0.826 for CDW 2014 dataset. These results indicate that the proposed
human detection system performs comparably better than state-of-the-art detectors such
as HOG, Deformable Parts Model (DPM), Aggregated Channel Features (ACF) and
Local Decorrelated Channel Features (LDCF).
