PROJECT TITLE :

Human Detection By Quadratic Classification On Subspace Of Extended Histogram Of Gradients - 2014

ABSTRACT:

This paper proposes a quadratic classification approach on the subspace of Extended Histogram of Gradients (ExHoG) for human detection. By investigating the restrictions of Histogram of Gradients (HG) and Histogram of Oriented Gradients (HOG), ExHoG is proposed as a replacement feature for human detection. ExHoG alleviates the problem of discrimination between a dark object against a bright background and vice versa inherent in HG. It additionally resolves a difficulty of HOG whereby gradients of opposite directions in the identical cell are mapped into the identical histogram bin. We tend to scale back the dimensionality of ExHoG using Asymmetric Principal Component Analysis (APCA) for improved quadratic classification. APCA additionally addresses the asymmetry issue in coaching sets of human detection where there are abundant fewer human samples than non-human samples. Our proposed approach is tested on 3 established benchmarking knowledge sets - INRIA, Caltech, and Daimler - using a modified Minimum Mahalanobis distance classifier. Results indicate that the proposed approach outperforms current state-of-the-art human detection ways.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

Image Classification Principal Component Analysis Gradient Methods Asymmetric Principal Component Analysis Histogram Of Gradients Human Detection Hog Dimension Reduction
PROJECT TITLE : Smartphone based Indoor Path Estimation and Localization without Human Intervention ABSTRACT: Many different kinds of indoor positioning systems have been developed as a result of the growing market interest in
PROJECT TITLE : Extract Human Mobility Patterns Powered by City Semantic Diagram ABSTRACT: The widespread use of global positioning system (GPS) devices has made it easier to access massive spatiotemporal trajectory data. This
PROJECT TITLE : Human Trajectory Forecasting in Crowds A Deep Learning Perspective ABSTRACT: Since the last few decades, human trajectory forecasting has been a field of active research due to the numerous real-world applications
PROJECT TITLE : HarMI Human Activity Recognition via Multi-Modality Incremental Learning ABSTRACT: Human activity recognition (HAR) is a topic that has received a lot of attention in recent years due to the proliferation of different
PROJECT TITLE : Exploring Human Mobility for Multi-pattern Passenger Prediction A Graph Learning Framework ABSTRACT: Predicting the flow of traffic is an essential component of an intelligent transportation system and, as such,