3,686 research outputs found
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
Pedestrian path prediction is an essential topic in computer vision and video
understanding. Having insight into the movement of pedestrians is crucial for
ensuring safe operation in a variety of applications including autonomous
vehicles, social robots, and environmental monitoring. Current works in this
area utilize complex generative or recurrent methods to capture many possible
futures. However, despite the inherent real-time nature of predicting future
paths, little work has been done to explore accurate and computationally
efficient approaches for this task. To this end, we propose a convolutional
approach for real-time pedestrian path prediction, CARPe. It utilizes a
variation of Graph Isomorphism Networks in combination with an agile
convolutional neural network design to form a fast and accurate path prediction
approach. Notable results in both inference speed and prediction accuracy are
achieved, improving FPS considerably in comparison to current state-of-the-art
methods while delivering competitive accuracy on well-known path prediction
datasets.Comment: AAAI-21 Camera Read
TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors
Tactile sensors provide useful contact data during the interaction with an
object which can be used to accurately learn to determine the stability of a
grasp. Most of the works in the literature represented tactile readings as
plain feature vectors or matrix-like tactile images, using them to train
machine learning models. In this work, we explore an alternative way of
exploiting tactile information to predict grasp stability by leveraging
graph-like representations of tactile data, which preserve the actual spatial
arrangement of the sensor's taxels and their locality. In experimentation, we
trained a Graph Neural Network to binary classify grasps as stable or slippery
ones. To train such network and prove its predictive capabilities for the
problem at hand, we captured a novel dataset of approximately 5000
three-fingered grasps across 41 objects for training and 1000 grasps with 10
unknown objects for testing. Our experiments prove that this novel approach can
be effectively used to predict grasp stability
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