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Real-time Online Action Detection Forests using Spatio-temporal Contexts
Seungryul Baek, Kwang In Kim, Tae-Kyun Kim
Online action detection (OAD) is challenging since 1) robust yet computationally expensive features cannot be straightforwardly used due to the real-time processing requirements an…
Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation
Andreas Doumanoglou, Vassileios Balntas, Rigas Kouskouridas +1
In this paper we tackle the problem of estimating the 3D pose of object instances, using convolutional neural networks. State of the art methods usually solve the challenging probl…
Kinematic-Layout-aware Random Forests for Depth-based Action Recognition
Seungryul Baek, Zhiyuan Shi, Masato Kawade +1
In this paper, we tackle the problem of 24 hours-monitoring patient actions in a ward such as "stretching an arm out of the bed", "falling out of the bed", where temporal movements…
Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection
Guillermo Garcia-Hernando, Tae-Kyun Kim
A human action can be seen as transitions between one's body poses over time, where the transition depicts a temporal relation between two poses. Recognizing actions thus involves…
Latent Bi-constraint SVM for Video-based Object Recognition
Yang Liu, Minh Hoai, Mang Shao +1
We address the task of recognizing objects from video input. This important problem is relatively unexplored, compared with image-based object recognition. To this end, we make the…
Effective Backscatter Approximation for Photometry in Murky Water
Chourmouzios Tsiotsios, Maria E. Angelopoulou, Andrew J. Davison +1
Shading-based approaches like Photometric Stereo assume that the image formation model can be effectively optimized for the scene normals. However, in murky water this is a very ch…
Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation
Qi Ye, Shanxin Yuan, Tae-Kyun Kim
Discriminative methods often generate hand poses kinematically implausible, then generative methods are used to correct (or verify) these results in a hybrid method. Estimating 3D…
Iterative Hough Forest with Histogram of Control Points for 6 DoF Object Registration from Depth Images
Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim
State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To reduce this dependency, we int…
Latent-Class Hough Forests for 6 DoF Object Pose Estimation
Rigas Kouskouridas, Alykhan Tejani, Andreas Doumanoglou +2
In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios. We adapt a state of the ar…