Intent Prediction in Human–Human Interactions

Baruah, Murchana; Banerjee, Bonny; Nagar, Atulya K.

doi:10.1109/thms.2023.3239648

Cited by 2 publications

(40 citation statements)

References 26 publications

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“…State trajectories can be mathematically modeled using appropriate stochastic processes. Trajectory modeling with destination information and intent inference has diverse applications in fields such as air/ground traffic [22], [23], missile systems [24], and human-machine interaction [25]. Various methods for trajectory modeling, prediction, and intent inference have been proposed [26]- [28].…”

Section: Several Key Results Have Been Achieved In the Context Of Linearmentioning

confidence: 99%

Regression with Set-Valued Categorical Predictors

Wang¹,

Ding²,

Yang³

2024

STAT SINICA

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We address the regression problem with a new form of data that arises from data privacy applications. Instead of point values, the observed explanatory variables are subsets containing each individual's original value. The classical regression analyses such as least squares are not applicable since the set-valued predictors only carry partial information about the original values. We propose a computationally efficient subset least squares method to perform regression for such data. We establish upper bounds of the prediction loss and risk in terms of the subset structure, the model structure, and the data dimension.The error rates are shown to be optimal under some common situations. Furthermore, we develop a model selection method to identify the most appropriate model for prediction.Experiment results on both simulated and real-world datasets demonstrate the promising performance of the proposed method.

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Section: Several Key Results Have Been Achieved In the Context Of Linearmentioning

confidence: 99%

Regression with Set-Valued Categorical Predictors

Wang¹,

Ding²,

Yang³

2024

STAT SINICA

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“…Unlike large AI models, the proposed models actively and selectively sample their environment, which allows them to be efficient in terms of model size (number of trainable parameters), data size (number of skeleton joints sampled at each glimpse on average), and training time. On comparing the proposed models (say, M2 and M3) with that in [ 11 ] (say, M1), our findings are as follows: The efficiency, and generation and classification accuracy on benchmark datasets of the three models (M1, M2, M3) are analyzed in both FP and TP environments. M1 yields the highest classification accuracy, followed closely by M2.…”

Section: Introductionmentioning

confidence: 95%

“…Models for two-person interaction generation (e.g., [ 11 , 15 , 29 , 40 ]), reaction generation (e.g., [ 28 , 30 , 41 , 42 ]), and two-person interaction recognition (e.g., [ 11 , 32 , 34 , 35 , 37 , 38 , 39 ]) using 3D skeletal data have been widely reported in the artificial intelligence (AI) and machine learning (ML) literature. Interaction generation is more challenging than reaction generation as the former requires generating the interaction sequence of both skeletons, while the latter requires generating the reaction sequence of one skeleton given the action sequence of the other.…”

Section: Related Workmentioning

confidence: 99%

“…Very few end-to-end AI/ML models perform both generation and recognition. In a model, generation and recognition can be performed either separately, such as in [ 41 ], or simultaneously, such as in [ 11 , 42 ] and our current work. In [ 11 ], both interacting skeletons in both FP and TP are generated by utilizing a variational recurrent neural network (RNN)-based model.…”

Section: Related Workmentioning

confidence: 99%

“…In artificial intelligence (AI) and related areas, human intent prediction has been extensively studied in the context of different applications such as assistive robotics (e.g., [ 7 ]), human-robot interaction (e.g., [ 8 ]), video and robotic surveillance (e.g., [ 9 ]), and autonomous driving (e.g., [ 10 ]). Following [ 11 ], we define “intent prediction” as the problem of simultaneously inferring the action/interaction class and generating the involved persons’ future body motions . Models that perform both generation and recognition of human-human interactions are scarce.…”

Section: Introductionmentioning

confidence: 99%

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Attention-Based Variational Autoencoder Models for Human–Human Interaction Recognition via Generation

Banerjee,

Baruah

2024

Sensors

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The remarkable human ability to predict others’ intent during physical interactions develops at a very early age and is crucial for development. Intent prediction, defined as the simultaneous recognition and generation of human–human interactions, has many applications such as in assistive robotics, human–robot interaction, video and robotic surveillance, and autonomous driving. However, models for solving the problem are scarce. This paper proposes two attention-based agent models to predict the intent of interacting 3D skeletons by sampling them via a sequence of glimpses. The novelty of these agent models is that they are inherently multimodal, consisting of perceptual and proprioceptive pathways. The action (attention) is driven by the agent’s generation error, and not by reinforcement. At each sampling instant, the agent completes the partially observed skeletal motion and infers the interaction class. It learns where and what to sample by minimizing the generation and classification errors. Extensive evaluation of our models is carried out on benchmark datasets and in comparison to a state-of-the-art model for intent prediction, which reveals that classification and generation accuracies of one of the proposed models are comparable to those of the state of the art even though our model contains fewer trainable parameters. The insights gained from our model designs can inform the development of efficient agents, the future of artificial intelligence (AI).

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Intent Prediction in Human–Human Interactions

Cited by 2 publications

References 26 publications

Regression with Set-Valued Categorical Predictors

Regression with Set-Valued Categorical Predictors

Attention-Based Variational Autoencoder Models for Human–Human Interaction Recognition via Generation

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