Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper WeBCT8.32
A Sample Pre-Mapping Method Enhancing Boosting for Object Detection
Ren, Haoyu, Chinese Acad. of Sciences
Hong, Xiaopeng, Harbin Inst. of Tech.
Heng, Cher Keng, Panasonic Singapore Lab. Pte Ltd
Liang, Luhong, Chinese Acad. of Sciences
Chen, Xilin, Chinese Acad. of Sciences
We propose a novel method to improve the training efficiency and accuracy of boosted classifiers for object detection.
The key step of the proposed method is a sample pre-mapping on original space by referring to the selected reference
sample before feeding into weak classifiers. The reference sample corresponds to an approximation of the optimal separating
hyper-plane in an implicit high dimensional space, so that the resulting classifier could achieve the performance
similar to kernel method, while spending the computation cost of linear classifier in both training and detection. We employ
two different non-linear mappings to verify the proposed method under boosting framework. Experimental results show
that the proposed approach achieves performance comparable with the common used methods on public datasets in both
pedestrian detection and car detection.
13:30-16:30, Paper WeBCT8.33
Context Inspired Pedestrian Detection in Far-field Videos
Ma, Wenhua, Chinese Acad. of Sciences
He, Peng, Chinese Acad. of Sciences
Lei, Huang, Chinese Acad. of Sciences
Liu, Changping, Chinese Acad. of Sciences
A novel pedestrian detection method that integrates context information with slide window search is proposed. The method
applies notions such as corner, motion, and appearance to localize pedestrians in far-field videos without performing bruteforce-search.
The corners direct attention to a set of conspicuous locations as the starting points for searching. And motion
detection restricts the searching area within the foreground mask. Based on the above two, slide window search is applied
to confirm the exact locations of pedestrians. Experiments demonstrate that the proposed method is efficient in detecting
pedestrians in far-field videos.
13:30-16:30, Paper WeBCT8.34
Theme-Based Multi-Class Object Recognition and Segmentation
Wu, Shilin, Chinese Acad. of Sciences
Geng, Jiajia, Chinese Acad. of Sciences
Zhu, Feng, Chinese Acad. of Sciences
In this paper, we propose a new theme-based CRF model and investigate its performance on class based pixel-wise segmentation
of images. By including the theme of an image, we also propose a new texture-environment potential to represent
texture environment of a pixel, which alone gives satisfactory recognition results. The pixel-wise segmentation accuracy
is remarkably improved by introducing texture potential. We compare our results to recent published results on the MSRC
21-class database and show that our theme-based CRF model significantly outperforms the current state-of-the-art. Especially,
by assigning a theme for each image, our model obtains greatly improved accuracy of structured classes with high
visual variability and fewer training examples, the accuracy of which is very low in most related works.
13:30-16:30, Paper WeBCT8.35
Boosted Sigma Set for Pedestrian Detection
Hong, Xiaopeng, Harbin Inst. of Tech.
Chang, Hong, Chinese Acad. of Sciences
Chen, Xilin, Chinese Acad. of Sciences
Gao, Wen, PeKing Univ.
This paper presents a new method to detect pedestrian in still image using Sigma sets as image region descriptors in the
boosting framework. Sigma set encodes second order statistics of an image region implicitly in the form of a point set.
Compared with the covariance matrix, the traditional second order statistics based region descriptor, which requires computationally
demanding operations based on Riemannian manifold, Sigma set preserves similar robustness and discrimi-
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