We develop an intermediate representation for deformable part models and show that this representation has favorable performance characteristics for multi-class problems when the number of classes is high. Our model uses sparse coding of part filters to represent each filter as a sparse linear combination of shared dictionary elements. This leads to a universal set of parts that are shared among all object classes. Reconstruction of the original part filter responses via sparse matrix-vector product reduces computation relative to conventional part filter convolutions. Our model is well suited to a parallel implementation, and we report a new GPU DPM implementation that takes advantage of sparse coding of part filters. The speed-up offered by our intermediate representation and parallel computation enable real-time DPM detection of 20 different object classes on a laptop computer.
Abstract. The current RoboCup Small Size League rules allow every team to set up their own global vision system as a primary sensor. This option, which is used by all participating teams, bears several organizational limitations and thus impairs the league's progress. Additionally, most teams have converged on very similar solutions, and have produced only few significant research results to this global vision problem over the last years. Hence the responsible committees decided to migrate to a shared vision system (including also sharing the vision hardware) for all teams by 2010. This system -named SSL-Vision -is currently developed by volunteers from participating teams. In this paper, we describe the current state of SSL-Vision, i. e. its software architecture as well as the approaches used for image processing and camera calibration, together with the intended process for its introduction and its use beyond the scope of the Small Size League.
Abstract-The LANdroids project requires robots to autonomously localize, track, and follow (a task also known as tethering) other robots or humans in an unknown environment with limited sensing abilities. In this paper, we present a localization and tethering approach that relies solely on wireless signal strength and robot odometry without requiring any known reference points in the domain. We introduce a datadriven, probabilistic model that maps received signal strength (RSS) values to real-world distance distributions and embed this model in a grid-based localization algorithm that successfully performs the LANdroids tethering task. We furthermore show, that it is possible to improve localization through the addition of a compass sensor and inter-robot information sharing.
Abstract-Being able to identify and localize objects is an important requirement for various humanoid robot applications. In this paper we present a method which uses PCA-SIFT in combination with a clustered voting scheme to achieve detection and localization of multiple objects in real-time video data. Our approach provides robustness against constraints that are common for humanoid vision systems such as perspective changes, partial occlusion, and motion blurring. We analyze and evaluate the performance of our method in two concrete humanoid testscenarios.I. INTRODUCTION SIFT (Scale Invariant Feature Transform) has been shown to be an effective descriptor for traditional object recognition applications in static images. In this paper, we propose and evaluate a method that uses PCA-SIFT [1] in combination with a clustered voting scheme to achieve detection and localization of multiple objects in video footage as it is typically collected by a humanoid robot's vision system. A common constraint of humanoid vision is the occurrence of frequent perspective changes and motion blurring which are often caused by the robot's walking motions. Our approach attempts to minimize the effects of these problems while still being applicable as a real-time algorithm.A flowchart of our approach is depicted in figure 1(a). First, a training video is recorded by observing the object from various perspectives. PCA-SIFT keypoints are then generated for each video frame. It should be emphasized that by training on an entire video sequence, we are able to capture impressions of the object from a continuous spectrum of poses, and under various configurations of lighting and video noise. This allows us to gain a more complete representation of the object in PCA-SIFT space compared to training from very few still images. The keypoint generation stage is followed by a manual annotation step which allows the user to approximate the boundary and position of the relevant object in the video. All keypoints that lie outside of the hand-annotated boundary are rejected. The annotation step is furthermore used to determine each keypoint's relative location toward the annotated object's center. All retained features build the initial training dataset. The dataset is then post-processed by a clustering algorithm to compress its overall size.During the recognition stage, we again generate PCA-SIFT keypoints for each incoming video frame. A nearest neighbor search is performed on the training dataset for each PCA-SIFT keypoint while enforcing a maximum distance threshold.
Abstract-After several years of developing multiple RoboCup small-size robot soccer teams, our CMDragons robot team achieved a highly successful level of performance, winning both the 2006 and 2007 competitions without losing a single game. Our small-size team consists of five executing wheeled robots with centralized, off-board perception and decision making. The decision making framework consists of a set of layered components, consisting of perception, evaluation and strategy, robot tactics and skills, and real-time navigation. In this paper, we present the strategy, action selection, and execution aspects of our architecture, with a focus on passing as an example of effective coordinated teamwork. The design enabled our robot team to score using multiple methods, from direct shooting up to 3D passes deflected in midair, resulting in a rich set of actions that were difficult for adversaries to counter. We provide several performance quantified claims supported by testing in our laboratory and in competition settings.
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