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Gait recognition using HMMs and dual discriminative observations for sub-dynamics analysis
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.We propose a new gait recognition method that combines holistic and model-based features. Both types of features are extracted automatically from gait silhouette sequences and their combination takes place by means of a pair of hidden Markov models. In the proposed system, the holistic features are initially used for capturing general gait dynamics whereas, subsequently, the model-based features are deployed for capturing more detailed sub-dynamics by refining upon the preceding general dynamics. Furthermore, the holistic and model-based features are suitably processed in order to improve the discriminatory capacity of the final system. The experimental results show that the proposed method exhibits performance advantages in comparison with popular existing methods
Improving Texture Categorization with Biologically Inspired Filtering
Within the domain of texture classification, a lot of effort has been spent
on local descriptors, leading to many powerful algorithms. However,
preprocessing techniques have received much less attention despite their
important potential for improving the overall classification performance. We
address this question by proposing a novel, simple, yet very powerful
biologically-inspired filtering (BF) which simulates the performance of human
retina. In the proposed approach, given a texture image, after applying a DoG
filter to detect the "edges", we first split the filtered image into two "maps"
alongside the sides of its edges. The feature extraction step is then carried
out on the two "maps" instead of the input image. Our algorithm has several
advantages such as simplicity, robustness to illumination and noise, and
discriminative power. Experimental results on three large texture databases
show that with an extremely low computational cost, the proposed method
improves significantly the performance of many texture classification systems,
notably in noisy environments. The source codes of the proposed algorithm can
be downloaded from https://sites.google.com/site/nsonvu/code.Comment: 11 page
Exploring Human Vision Driven Features for Pedestrian Detection
Motivated by the center-surround mechanism in the human visual attention
system, we propose to use average contrast maps for the challenge of pedestrian
detection in street scenes due to the observation that pedestrians indeed
exhibit discriminative contrast texture. Our main contributions are first to
design a local, statistical multi-channel descriptorin order to incorporate
both color and gradient information. Second, we introduce a multi-direction and
multi-scale contrast scheme based on grid-cells in order to integrate
expressive local variations. Contributing to the issue of selecting most
discriminative features for assessing and classification, we perform extensive
comparisons w.r.t. statistical descriptors, contrast measurements, and scale
structures. This way, we obtain reasonable results under various
configurations. Empirical findings from applying our optimized detector on the
INRIA and Caltech pedestrian datasets show that our features yield
state-of-the-art performance in pedestrian detection.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technology (TCSVT
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