Online Goal Recognition using Path Signature and Dynamic Time Warping
Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Recent work addresses these challenges by using custom state-space representations and metrics to compare observations against hypotheses. However, these approaches often overlook well-established encoding techniques used in other domains that offer substantial advantages. This paper introduces a novel method for online goal recognition that leverages path signatures, a compact, expressive representation of rough path theory that efficiently captures key semantic features of trajectories, enabling more meaningful comparisons between them. Experiments show that our method consistently outperforms the state of the art in predictive accuracy and online planning efficiency, while remaining competitive offline.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sparse arrays of signatures for online character recognition
In mathematics the signature of a path is a collection of iterated integrals, commonly used for solving differential equations. We show that the path signature, used as a set of features for consumption by a convolutiona…
On handwriting pressure normalization for interoperability of different acquisition stylus
In this paper, we present a pressure characterization and normalization procedure for online handwritten acquisition. Normalization process has been tested in biometric recognition experiments (identification and verific…
The effect of fatigue on the performance of online writer recognition
Background: The performance of biometric modalities based on things done by the subject, like signature and text-based recognition, may be affected by the subject state. Fatigue is one of the conditions that can signific…
Dynamic Time WarpingQuantizationToward high-performance online HCCR: a CNN approach with DropDistortion, path signature and spatial stochastic max-pooling
This paper presents an investigation of several techniques that increase the accuracy of online handwritten Chinese character recognition (HCCR). We propose a new training strategy named DropDistortion to train a deep co…
Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition
Landmark-based human action recognition in videos is a challenging task in computer vision. One key step is to design a generic approach that generates discriminative features for the spatial structure and temporal dynam…
Action ClassificationAction RecognitionAction Recognition In VideosTemporal Action Localization