Human Detection Paper Review from ICCV 2009

Pramod Sharma


Abstract

There are two papers : 1. Human Detection Using Partial Least Squares Analysis, William Robson Schwartz, Aniruddha Kembhavi, David Harwood, Larry S. Davis (ICCV 09)

In this paper we describe a human detection method that augments widely used edge-based features with texture and color information, providing us with a much richer descriptor set. We employ Partial Least Squares (PLS) analysis, an efficient dimensionality reduction technique, one which preserves significant discriminative information, to project the data onto a much lower dimensional subspace (20 dimensions, reduced from the original 170,000).

2. An HOG-LBP Human Detector with Partial Occlusion Handling, Xioayu Wang, Tony X. Han, Shuicheng Yan (ICCV 09)

By combining Histograms of Oriented Gradients (HOG) and Local Binary Pattern (LBP) as the feature set, we pro- pose a novel human detection approach capable of handling partial occlusion. With the help of the augmented HOG-LBP feature and the global-part occlu-sion handling method, we achieve a detection rate of 91.3% with FPPW= 10-6, 94.7% with FPPW= 10-5, and 97.9% with FPPW= 10-4 on the INRIA dataset, which, to our best knowledge, is the best human detection performance on the INRIA dataset. The global-part occlusion handling method is further validated using synthesized occlusion data con-structed from the INRIA and Pascal dataset.


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