Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks

Kambara, Motonari; Sugiura, Komei

doi:10.48550/arxiv.2207.09083

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…The Dunn index: The Dunn index [ 51 ] aims to maximize the distance between clusters while minimizing the distance within clusters. Let

denote the maximum distance between any 2 data points, k and

, assigned to the same cluster:

…”

Section: Applied Scoresmentioning

confidence: 99%

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Ekemeyong Awong,

Zielinska

2023

Sensors

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The objective of this article is to develop a methodology for selecting the appropriate number of clusters to group and identify human postures using neural networks with unsupervised self-organizing maps. Although unsupervised clustering algorithms have proven effective in recognizing human postures, many works are limited to testing which data are correctly or incorrectly recognized. They often neglect the task of selecting the appropriate number of groups (where the number of clusters corresponds to the number of output neurons, i.e., the number of postures) using clustering quality assessments. The use of quality scores to determine the number of clusters frees the expert to make subjective decisions about the number of postures, enabling the use of unsupervised learning. Due to high dimensionality and data variability, expert decisions (referred to as data labeling) can be difficult and time-consuming. In our case, there is no manual labeling step. We introduce a new clustering quality score: the discriminant score (DS). We describe the process of selecting the most suitable number of postures using human activity records captured by RGB-D cameras. Comparative studies on the usefulness of popular clustering quality scores—such as the silhouette coefficient, Dunn index, Calinski–Harabasz index, Davies–Bouldin index, and DS—for posture classification tasks are presented, along with graphical illustrations of the results produced by DS. The findings show that DS offers good quality in posture recognition, effectively following postural transitions and similarities.

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“…The Dunn index: The Dunn index [ 51 ] aims to maximize the distance between clusters while minimizing the distance within clusters. Let

denote the maximum distance between any 2 data points, k and

, assigned to the same cluster:

…”

Section: Applied Scoresmentioning

confidence: 99%

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Ekemeyong Awong,

Zielinska

2023

Sensors

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“…The aim is to localize and generate a relationship between them. Prediction of a human-object interaction requires knowledge about objects (e.g for what they can be used), possible interactions [74]. Xu et al [75] proposed a scene perception architecture that predicted human tasks which involve objects.…”

Section: Types Of Interactionsmentioning

confidence: 99%

Predicting Human Activity – State of the Art

Awong¹,

Zielińska²

2023

PAR

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Predicting human actions is a very actual research field. Artificial intelligence methods are commonly used here. They enable early recognition and classification of human activities. Such knowledge is extremely needed in the work on robots and other interactive systems that communicate and cooperate with people. This ensures early reactions of such devices and proper planning of their future actions. However, due to the complexity of human actions, predicting them is a difficult task. In this article, we review state-of-the-art methods and summarize recent advances in predicting human activity. We focus in particular on four approaches using machine learning methods, namely methods using: artificial neural networks, support vector machines, probabilistic models and decision trees. We discuss the advantages and disadvantages of these approaches, as well as current challenges related to predicting human activity. In addition, we describe the types of sensors and data sets commonly used in research on predicting and recognizing human actions. We analyze the quality of the methods used, based on the prediction accuracy reported in scientific articles. We describe the importance of the data type and the parameters of machine learning models. Finally, we summarize the latest research trends. The article is intended to help in choosing the right method of predicting human activity, along with an indication of the tools and resources necessary to effectively achieve this goal.

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Relational Future Captioning Model for Explaining Likely Collisions in Daily Tasks

Cited by 2 publications

References 22 publications

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification

Predicting Human Activity – State of the Art

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