A Network of Dynamic Probabilistic Models for Human Interaction Analysis

Abstract

Abstract-We propose a novel method of analyzing human interactions based on the walking trajectories of human subjects, which provide elementary and necessary components for understanding and interpretation of complex human interactions in visual surveillance tasks. Our principal assumption is that an interaction episode is composed of meaningful small unit interactions, which we call "sub-interactions." We model each sub-interaction by a dynamic probabilistic model and propose a modified factorial hidden Markov model (HMM) with factored observations. The complete interaction is represented with a network of dynamic probabilistic models (DPMs) by an ordered concatenation of sub-interaction models. The rationale for this approach is that it is more effective in utilizing common components, i.e., sub-interaction models, to describe complex interaction patterns. By assembling these sub-interaction models in a network, possibly with a mixture of different types of DPMs, such as standard HMMs, variants of HMMs, dynamic Bayesian networks, and so on, we can design a robust model for the analysis of human interactions. We show the feasibility and effectiveness of the proposed method by analyzing the structure of network of DPMs and its success on four different databases: a self-collected dataset, Tsinghua University's dataset, the public domain CAVIAR dataset, and the Edinburgh Informatics Forum Pedestrian dataset.Index Terms-Dynamic Bayesian network, human interaction analysis, network of dynamic probabilistic models, subinteractions, video surveillance.

Enhanced Deep Learning Models for Efficient Stroke Detection Using MRI Brain Imagery

“…Karthik et al [11] studied neuroimaging techniques and explored deep learning towards stroke diagnosis. In [12] an ensemble learning approach is preferred using deep learning models that are used as regression models to diagnose brain abnormalities. In [13] proposed a methodology for understanding existing techniques based on deep architectures for MRI image analysis.…”

A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching

“…In Figure 2, it illustrates a failure case when the source image and the target image are swapped. Many computer vision tasks have been applied and developed in real life [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Because deep neural networks (DNNs) have shown impressive performance in real-world computer vision tasks [25][26][27][28], several approaches apply DNNs to overcome the limitation of traditional computer vision methods for matching the images.…”