## 1 Introduction  
The current era is the age of advanced technology and research in various domains of information technology, computer vision, and artificial intelligence. These domains open the way to develop smart technologies and smart tools with the help of existing cameras and motion-based sensors. These smart tools enable a way to find solutions in various domains, one of which is to find and estimate human behavior that proves to be very useful for improving the lifelog of humans and advancement in the living standard of society. However, some challenges and issues still exist in this domain such as noise reduction, posture estimation, behavior mining, cost-efficient system, pedestrian behavior mining, data optimization, and the classification of various events and activities. These issues still need to be addressed with the help of previous knowledge and current technology.  
Currently, motion sensors and camera-based human motion detection techniques are used in various domains and smart applications [1; 2; 3]. While these applications are a part of smart systems such as smart homes, the internet of things [4; 5], machine learning-based prediction systems [6], information security and encryption [7], smart emergency systems [8], educational and managements system, e-learning approaches, smart transport systems, and smart medical systems, in the security domain, it may help to find the anomaly and normal behavior of human and even nonhuman objects [9; 10; 11], while in the education system users can monitor the behavior of students. In the sports domain, we also find the behavior of the players and crowd [12]. Similarly, in the medical domain using the EBP system, we can predict the behavior of patients, doctors, and visitors [13], while in smart homes or internet of things-based systems we can predict the nonhuman-based objects' behavior and as well as human behavior.  
Thus, in this article, we designed a robust approach for extrinsic behavior prediction of the pedestrian in indoor and outdoor crowd scenes. Initially, indoor and outdoor crowd-based data are considered as the input for the proposed system; then human silhouette extraction, human verification, crowd analysis, and crowd clustering are the next phase of the system. After the extraction of a human silhouette, the next step is to extract the features to predict more accurate human behavior. That is, three main features are extracted from all the datasets such as deep flow, force flow matrix, and interaction force. To minimize the repetition of features data and reduce the use of computational resources, we use the graph mining approach and to find extrinsic behavior prediction of pedestrians; the maximum entropy Markov model [14; 15; 16] is used, which is one of the advanced models to predict more accurate over complex datasets. Three main publicly available datasets are used, namely avenue_dataset, crowd-activity_UMN_dataset, and A Day on Campus (ADOC). Our contributions are as follows:  
* We proposed a robust human silhouette extraction approach and human verification method in crowded outdoor and indoor environments.
* We constructed a graph mining optimization approach and maximum entropy Markov model, which presented relevant information along with classifying pedestrian behaviors for complicated crowd indoor and outdoor data.
* Furthermore, three major benchmark datasets were thoroughly evaluated, providing a remarkable performance in comparison with other state-of-the-art approaches.  
The rest of this paper is structured as follows. We evaluate the associated methods of pedestrian behavior mining in Section 2. Section 3 contains the proposed EPB methodology and features extraction approach along with graph mining and the maximum entropy Markov model. Section 4 describes the tentative design and the outcomes of the evaluation with present recognized statistical state-of-the-art approaches. Finally, Section 5 concludes the paper and suggests some future trends.