Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT8.32
Action Detection in Crowded Videos using Masks
Guo, Ping, Beijing Jiaotong Univ.
Miao, Zhenjiang, Beijing Jiaotong Univ.
In this paper, we investigate the task of human action detection in crowded videos. Different from action analysis in clean
scenes, action detection in crowded environments is difficult due to the cluttered backgrounds, high densities of people
and partial occlusions. This paper proposes a method for action detection based on masks. No human segmentation or
tracking technique is required. To cope with the cluttered and crowded backgrounds, shape and motion templates are built
and the shape templates are used as masks for feature refining. In order to handle the partial occlusion problem, only the
moving body parts in each motion are involved in action training. Experiments using our approach are conducted on the
CMU dataset with encouraging results.
13:30-16:30, Paper TuBCT8.33
3D Model based Vehicle Tracking using Gradient based Fitness Evaluation under Particle Filter Framework
Zhang, Zhaoxiang, Beihang Univ.
Huang, Kaiqi, Chinese Academy of Sciences
Tan, Tieniu, Chinese Academy of Sciences
Wang, Yunhong, Beihang Univ.
We address the problem of 3D model based vehicle tracking from monocular videos of calibrated traffic scenes. A 3D
wire-frame model is set up as prior information and an efficient fitness evaluation method based on image gradients is introduced
to estimate the fitness score between the projection of vehicle model and image data, which is then combined
into a particle filter based framework for robust vehicle tracking. Numerous experiments are conducted and experimental
results demonstrate the effectiveness of our approach for accurate vehicle tracking and robustness to noise and occlusions.
13:30-16:30, Paper TuBCT8.34
Recovering 3D Shape and Light Source Positions from Non-Planar Shadows
Yamashita, Yukihiro, Nagoya Inst. of Tech.
Sakaue, Fumihiko, Nagoya Inst. of Tech.
Sato, Jun, Nagoya Inst. of Tech.
Recently, Shadow Graph has been proposed for recovering 3D shapes from shadows projected on curved surfaces. Unfortunately,
this method requires a large computational cost. In this paper, we introduce 1D Shadow Graph which can be
used for recovering 3D shapes with much smaller computational costs. We also extend our method, so that we can estimate
both 3D shapes and light source positions simultaneously under a condition where 3D shapes and light sources are unknown.
13:30-16:30, Paper TuBCT8.35
3D Contour Model Creation for Stereo-Vision Systems
Maruyama, Kenichi, National Inst. of Advanced Industrial Science and Tech.
Kawai, Yoshihiro, National Inst. of Advanced Industrial Science and Tech.
Tomita, Fumiaki, National Inst. of Advanced Industrial Science and Tech.
The present paper describes a method for automatic 3D contour model creation for stereo-vision systems. The object
model is a triangular surface mesh and a set of aspect models, which consists of model features and model points. Model
features and model points are generated using 3D contours, which are estimated by the projected images of the triangular
surface mesh from multiple discrete viewing directions. Using a non-photorealistic rendering approach, we extract not
only the outer contours but also the inner contours of the projected images. Using both the inner and outer contours of the
projected images, we create the object model which has 3D inner contour features and 3D contour generator features. Experimental
results obtained using the 3D localization algorithm demonstrate the effectiveness of the proposed model.
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