Wednesday, May 28, 2014

Lab meeting May 29, 2014 (Kung-Hung Lu): Finding Group Interactions in Social Clutter

Title: Finding Group Interactions in Social Clutter

Authors: Ruonan Li, Parker Porfilio, Todd Zickler

We consider the problem of finding distinctive social interactions involving groups of agents embedded in larger social gatherings. Given a pre-defined gallery of short exemplar interaction videos, and a long input video of a large gathering (with approximately-tracked agents), we identify within the gathering small sub-groups of agents exhibiting social interactions that resemble those in the exemplars. The participants of each detected group interaction are localized in space; the extent of their interaction is localized in time; and when the gallery of exemplars is annotated with group-interaction categories, each detected interaction is classified into one of the pre-defined categories. Our approach represents group behaviors by dichotomous collections
of descriptors for (a) individual actions, and (b) pairwise interactions; and it includes efficient algorithms for
optimally distinguishing participants from by-standers in every temporal unit and for temporally localizing the extent of the group interaction. Most importantly, the method is generic and can be applied whenever numerous interacting agents can be approximately tracked over time. We evaluate the approach using three different video collections, two that involve humans and one that involves mice.

In: Computer Vision and Pattern Recognition(CVPR), 2013 IEEE Conference on. IEEE, 2013


Wednesday, May 21, 2014

Lab meeting May 22, 2014 (Chun-Kai Chang): Communication Adaptive Multi-Robot Simultaneous Localization and Tracking via Hybrid Measurement and Belief Sharing

Title: Communication Adaptive Multi-Robot Simultaneous Localization and Tracking via Hybrid Measurement and Belief Sharing

Authors: Chun-Kai Chang, Chun-Hua Chang and Chieh-Chih Wang

Existing multi-robot cooperative perception solutions can be mainly classified into two categories, measurement-based and belief-based, according to the information shared among robots. With well-controlled communication, measurement-based approaches are expected to achieve theoretically optimal estimates while belief-based approaches are not because the cross-correlations between beliefs are hard to be perfectly estimated in practice. Nevertheless, belief-based approaches perform relatively stable under unstable communication as a belief contains the information of multiple previous measurements. Motivated by the observation that measurement sharing and belief sharing are respectively superior in different conditions, in this paper a hybrid algorithm, communication adaptive multi-robot simultaneous localization and tracking (ComAd MR-SLAT), is proposed to combine the advantages of both. To tackle the unknown or unstable communication conditions, the information to share is decided by maximizing the expected uncertainty reduction online, based on which the algorithm dynamically alternates between measurement sharing and belief-sharing without information loss or reuse. The proposed ComAd MR-SLAT is evaluated in communication conditions with different packet loss rates and bursty loss lengths. In our experiments, ComAd MR-SLAT outperforms measurement-based and belief-based MR-SLAT in accuracy. The experimental results demonstrate the effectiveness of the proposed hybrid algorithm and exhibit that ComAd MR-SLAT is robust under different communication conditions.

IEEE International Conference on Robotics and Automation, 2014.

Wednesday, May 14, 2014

Lab meeting May 15, 2014 (Yun-Jun Shen): Robust Monocular Epipolar Flow Estimation

Title: Robust Monocular Epipolar Flow Estimation

Authors: Koichiro Yamaguchi, David McAllester and Raquel Urtasun

We consider the problem of computing optical flow in monocular video taken from a moving vehicle. In this setting, the vast majority of image flow is due to the vehicle’s ego-motion. We propose to take advantage of this fact and estimate flow along the epipolar lines of the egomotion. Towards this goal, we derive a slanted-plane MRF model which explicitly reasons about the ordering of planes and their physical validity at junctions. Furthermore, we present a bottom-up grouping algorithm which produces over-segmentations that respect flow boundaries. We demonstrate the effectiveness of our approach in the challenging KITTI flow benchmark achieving half the error of the best competing general flow algorithm and one third of the error of the best epipolar flow algorithm.

In: Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. IEEE, 2013


Tuesday, May 06, 2014

Lab meeting May 8, 2014 (Bang-Cheng Wang): Robust feedback control of ZMP-based gait for the humanoid robot Nao

Authors: J.J. Alcaraz-Jiménez, D. Herrero-Pérez and H. Martínez-Barberá

Numerous approaches have been proposed to generate well-balanced gaits in biped robots that show excellent performance in simulated environments. However, in general, the dynamic balance of the robots decreases dramatically when these methods are tested in physical  platforms. Since humanoid robots are intended to collaborate with humans and operate in everyday environments, it is of paramount importance to test such approaches both in physical platforms and under severe conditions. In this work, the special characteristics of the Nao  humanoid platform are analyzed and a control system that allows robust walking and  disturbance rejection is proposed. This approach combines the zero moment point (ZMP) stability criterion with angular momentum suppression and step timing control. The proposed method is especially suitable for platforms with limited computational resources and sensory and sensory-motor capabilities.

In: vol. 32