Egocentric View Research Articles

Detecting Groups and Estimating F-Formations for Social Human–Robot Interactions

The ability of a robot to detect and join groups of people is of increasing importance in social contexts, and for the collaboration between teams of humans and robots. In this paper, we propose a framework, autonomous group interactions for robots (AGIR), that endows a robot with the ability to detect such groups while following the principles of F-formations. Using on-board sensors, this method accounts for a wide spectrum of different robot systems, ranging from autonomous service robots to telepresence robots. The presented framework detects individuals, estimates their position and orientation, detects groups, determines their F-formations, and is able to suggest a position for the robot to enter the social group. For evaluation, two simulation scenes were developed based on the standard real-world datasets. The 1st scene is built with 20 virtual agents (VAs) interacting in 7 different groups of varying sizes and 3 different formations. The 2nd scene is built with 36 VAs, positioned in 13 different groups of varying sizes and 6 different formations. A model of a Pepper robot is used in both simulated scenes in randomly generated different positions. The ability for the robot to estimate orientation, detect groups, and estimate F-formations at various locations is used to determine the validation of the approaches. The obtained results show a high accuracy within each of the simulated scenarios and demonstrates that the framework is able to work from an egocentric view with a robot in real time.

Head-Mounted Augmented Reality Platform for Markerless Orthopaedic Navigation.

Visual augmented reality (AR) has the potential to improve the accuracy, efficiency and reproducibility of computer-assisted orthopaedic surgery (CAOS). AR Head-mounted displays (HMDs) further allow non-eye-shift target observation and egocentric view. Recently, a markerless tracking and registration (MTR) algorithm was proposed to avoid the artificial markers that are conventionally pinned into the target anatomy for tracking, as their use prolongs surgical workflow, introduces human-induced errors, and necessitates additional surgical invasion in patients. However, such an MTR-based method has neither been explored for surgical applications nor integrated into current AR HMDs, making the ergonomic HMD-based markerless AR CAOS navigation hard to achieve. To these aims, we present a versatile, device-agnostic and accurate HMD-based AR platform. Our software platform, supporting both video see-through (VST) and optical see-through (OST) modes, integrates two proposed fast calibration procedures using a specially designed calibration tool. According to the camera-based evaluation, our AR platform achieves a display error of 6.31 ± 2.55 arcmin for VST and 7.72 ± 3.73 arcmin for OST. A proof-of-concept markerless surgical navigation system to assist in femoral bone drilling was then developed based on the platform and Microsoft HoloLens 1. According to the user study, both VST and OST markerless navigation systems are reliable, with the OST system providing the best usability. The measured navigation error is 4.90 ± 1.04 mm, 5.96 ± 2.22 ° for the VST system, and 4.36 ± 0.80 mm, 5.65 ± 1.42 ° for the OST system.

Detection of Road Accidents Using Synthetically Generated Multi-Perspective Accident Videos

Road accidents are often caused by short abnormal events, including traffic violations, abrupt change in vehicular motion, driver fatigue, etc. Observing an accident event from the right camera perspective plays a crucial role while detecting accidents. However, it may not be possible to capture such abnormal events from a limited camera perspective. We present a deep learning framework to analyze the accident events recorded from multiple perspectives. First, we estimate feature similarity in videos recorded from multiple perspectives. We then divided the video samples into high and low feature similarity groups. Next, we extract spatio-temporal features from each group using two-branch DCNNs and fuse them using a rank-based weighted average pooling strategy followed by classification. We present a new road accident video dataset (MP-RAD), where each accident event is synthetically generated and captured from five independent camera perspectives using a computer gaming platform. Most of the existing road accident datasets use egocentric views or they are captured in fixed camera setups. However, our dataset is large and multi-perspective that can be used to validate ITS-related tasks such as accident detection, accident localization, traffic monitoring, etc. The dataset contains 400 accident events with a total of 2000 videos. We provide temporal annotations of all videos. The proposed framework and the dataset have been cross-validated with latest accident detection baselines trained on real-world road accident videos and vice-versa. The sub-optimal detection accuracy obtained using the baselines indicates that the proposed framework and the dataset can be useful for ITS related research. Code and dataset is available at: https://github.com/draxler1/MP-RAD-Dataset-ITS-

Video Captioning Based on Both Egocentric and Exocentric Views of Robot Vision for Human-Robot Interaction

Robot vision provides the most important information to robots so that they can read the context and interact with human partners successfully. Moreover, to allow humans recognize the robot’s visual understanding during human-robot interaction (HRI), the best way is for the robot to provide an explanation of its understanding in natural language. In this paper, we propose a new approach by which to interpret robot vision from an egocentric standpoint and generate descriptions to explain egocentric videos particularly for HRI. Because robot vision equals to egocentric video on the robot’s side, it contains as much egocentric view information as exocentric view information. Thus, we propose a new dataset, referred to as the global, action, and interaction (GAI) dataset, which consists of egocentric video clips and GAI descriptions in natural language to represent both egocentric and exocentric information. The encoder-decoder based deep learning model is trained based on the GAI dataset and its performance on description generation assessments is evaluated. We also conduct experiments in actual environments to verify whether the GAI dataset and the trained deep learning model can improve a robot vision system

Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots.

Collaborative robots are currently deployed in professional environments, in collaboration with professional human operators, helping to strike the right balance between mechanization and manual intervention in manufacturing processes required by Industry 4.0. In this paper, the contribution of gesture recognition and pose estimation to the smooth introduction of cobots into an industrial assembly line is described, with a view to performing actions in parallel with the human operators and enabling interaction between them. The proposed active vision system uses two RGB-D cameras that record different points of view of gestures and poses of the operator, to build an external perception layer for the robot that facilitates spatiotemporal adaptation, in accordance with the human's behavior. The use-case of this work is concerned with LCD TV assembly of an appliance manufacturer, comprising of two parts. The first part of the above-mentioned operation is assigned to a robot, strengthening the assembly line. The second part is assigned to a human operator. Gesture recognition, pose estimation, physical interaction, and sonic notification, create a multimodal human-robot interaction system. Five experiments are performed, to test if gesture recognition and pose estimation can reduce the cycle time and range of motion of the operator, respectively. Physical interaction is achieved using the force sensor of the cobot. Pose estimation through a skeleton-tracking algorithm provides the cobot with human pose information and makes it spatially adjustable. Sonic notification is added for the case of unexpected incidents. A real-time gesture recognition module is implemented through a Deep Learning architecture consisting of Convolutional layers, trained in an egocentric view and reducing the cycle time of the routine by almost 20%. This constitutes an added value in this work, as it affords the potential of recognizing gestures independently of the anthropometric characteristics and the background. Common metrics derived from the literature are used for the evaluation of the proposed system. The percentage of spatial adaptation of the cobot is proposed as a new KPI for a collaborative system and the opinion of the human operator is measured through a questionnaire that concerns the various affective states of the operator during the collaboration.

Shaftesbury and the Stoic Roots of Modern Aesthetics

Rather than reading Shaftesbury in anticipation of later forms of disinterestedness, this essay seeks to unpack the larger significance of his aesthetics by tracing his ideas back to their ancient sources. This essay looks to the venerable tradition of world contemplation. It argues that Shaftesbury advances a specifically Stoic model of world contemplation in The Moralists. The text’s principal concern is not with this or that beautiful object but with the whole of which it and the viewer are indivisibly a part; its aim is not so much to account for how we perceive beauty as to foster a characteristically Stoic orientation toward the world, one in which we overcome our egocentric view of things and align ourselves with the natural workings of the world or universe in its entirety. Far from being ‘autonomous’ from the rest of life, the Stoic world contemplation Shaftesbury advocates entails a robust affirmation of existence, clear-eyed gratitude for being part of the universe, whatever the challenges and however fleeting our time in it may be.

Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots

The ability to distinguish between the self and the background is of paramount importance for robotic tasks. The particular case of hands, as the end effectors of a robotic system that more often enter into contact with other elements of the environment, must be perceived and tracked with precision to execute the intended tasks with dexterity and without colliding with obstacles. They are fundamental for several applications, from Human–Robot Interaction tasks to object manipulation. Modern humanoid robots are characterized by high number of degrees of freedom which makes their forward kinematics models very sensitive to uncertainty. Thus, resorting to vision sensing can be the only solution to endow these robots with a good perception of the self, being able to localize their body parts with precision. In this paper, we propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. It is known that CNNs require a huge amount of data to be trained. To overcome the challenge of labeling real-world images, we propose the use of simulated datasets exploiting domain randomization techniques. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy. We focus our attention on developing a methodology that requires low amounts of data to achieve reasonable performance while giving detailed insight on how to properly generate variability in the training dataset. Moreover, we analyze the fine-tuning process within the complex model of Mask-RCNN, understanding which weights should be transferred to the new task of segmenting robot hands. Our final model was trained solely on synthetic images and achieves an average IoU of 82% on synthetic validation data and 56.3% on real test data. These results were achieved with only 1000 training images and 3 h of training time using a single GPU.

Collision Avoidance Using Deep Learning-Based Monocular Vision

Autonomous driving technologies, including monocular vision-based approaches, are in the forefront of industrial and research communities, since they are expected to have a significant impact on economy and society. However, they have limitations in terms of crash avoidance because of the rarity of labeled data for collisions in everyday traffic, as well as due to the complexity of driving situations. In this work, we propose a simple method based solely on monocular vision to overcome the data scarcity problem and to promote forward collision avoidance systems. We exploit state-of-the-art deep learning-based optical flow and monocular depth estimation methods, as well as object detection to estimate the speed of the ego-vehicle and to identify the lead vehicle, respectively. The proposed method utilizes car stop situations as collision surrogates to obtain data for time to collision estimation. We evaluate this approach on our own driving videos, collected using a spherical camera and smart glasses. Our results indicate that similar accuracy can be achieved on both video sources: the external road view from the car’s, and the ego-centric view from the driver’s perspective. Additionally, we set forth the possibility of using spherical cameras as opposed to traditional cameras for vision-based automotive sensing.

Semantic MapNet: Building Allocentric Semantic Maps and Representations from Egocentric Views

We study the task of semantic mapping – specifically, an embodied agent (a robot or an egocentric AI assistant) is given a tour of a new environment and asked to build an allocentric top-down semantic map (‘what is where?’) from egocentric observations of an RGB-D camera with known pose (via localization sensors). Importantly, our goal is to build neural episodic memories and spatio-semantic representations of 3D spaces that enable the agent to easily learn subsequent tasks in the same space – navigating to objects seen during the tour (‘Find chair’) or answering questions about the space (‘How many chairs did you see in the house?’). Towards this goal, we present Semantic MapNet (SMNet), which consists of: (1) an Egocentric Visual Encoder that encodes each egocentric RGB-D frame, (2) a Feature Projector that projects egocentric features to appropriate locations on a floor-plan, (3) a Spatial Memory Tensor of size floor-plan length×width×feature-dims that learns to accumulate projected egocentric features, and (4) a Map Decoder that uses the memory tensor to produce semantic top-down maps. SMNet combines the strengths of (known) projective camera geometry and neural representation learning. On the task of semantic mapping in the Matterport3D dataset, SMNet significantly outperforms competitive baselines by 4.01−16.81% (absolute) on mean-IoU and 3.81−19.69% (absolute) on Boundary-F1 metrics. Moreover, we show how to use the spatio-semantic allocentric representations build by SMNet for the task of ObjectNav and Embodied Question Answering. Project page: https://vincentcartillier.github.io/smnet.html.

Quantifying Prosthetic and Intact Limb Use in Upper Limb Amputees via Egocentric Video: An Unsupervised, At-Home Study

Analysis of the manipulation strategies employed by upper-limb prosthetic device users can provide valuable insights into the shortcomings of current prosthetic technology or therapeutic interventions. Typically, this problem has been approached with survey or lab-based studies, whose prehensile-grasp-focused results do not necessarily give accurate representations of daily activity. In this work, we capture prosthesis-user behavior in the unstructured and familiar environments of the participants own homes. Compact head-mounted video cameras recorded ego-centric views of the hands during self-selected household chores. Over 60 hours of video was recorded from 8 persons with unilateral amputation or limb difference (6 transradial, 1 transhumeral, 1 shoulder). Of this, almost 16 hours of video data was analyzed by human experts using the 22-category `TULIP' custom manipulation taxonomy, producing the type and duration of over 27,000 prehensile and non-prehensile manipulation tags on both upper limbs, permitting a level of objective analysis not previously possible with this population. Our analysis included unique observations on non-prehensile manipulations occurrence, determining that 79% of transradial body-powered device manipulations were non-prehensile, compared to 60% for transradial myoelectric devices. Conversely, only 16-19% of intact limb activity was non-prehensile. Additionally, multi-grasp terminal devices did not lead to increased activity compared to 1DOF devices.

The Effects of Interface Views on Performing Aerial Telemanipulation Tasks Using Small UAVs

This paper presents a human-robot interaction (HRI) study of a dedicated Mission Specialist interface for performing telemanipulation tasks using a small unoccupied aerial vehicle (UAV). Current literature suggests that the successful completion of aerial manipulation tasks in real-world environments requires human input due to challenges in autonomous perception and control. Visual information of the remote environment in a telemanipulation interface can significantly affect performance under direct control; however, the effects of interface visualizations on task performance have not been studied for UAV telemanipulation. This work evaluated the effects of interface viewpoint on aerial manipulation task performance. The interfaces evaluated in this study included video streams from cameras located onboard the UAV, including: (i) a manipulator egocentric view, (ii) a manipulator exocentric view, and (iii) a combination of egocentric and exocentric views. A total of 36 participants completed three different manipulation tasks using all three interface conditions. The observations and results showed that both the exocentric and mixed view configurations contributed to improved task performance over an egocentric-only interface. Further, this study resulted in data regarding view use, view effectiveness, and task type that can be used for further developing interfacing for aerial manipulators that change and adapt to the environment and task.

Multimodal multi-pedestrian path prediction for autonomous cars

Accurate prediction of the future position of pedestrians in traffic scenarios is required for safe navigation of an autonomous vehicle but remains a challenge. This concerns, in particular, the effective and efficient multimodal prediction of most likely trajectories of tracked pedestrians from egocentric view of self-driving car. In this paper, we present a novel solution, named M2P3, which combines a conditional variational autoencoder with recurrent neural network encoder-decoder architecture in order to predict a set of possible future locations of each pedestrian in a traffic scene. The M2P3 system uses a sequence of RGB images delivered through an internal vehicle-mounted camera for egocentric vision. It takes as an input only two modes, that are past trajectories and scales of pedestrians, and delivers as an output the three most likely paths for each tracked pedestrian. Experimental evaluation of the proposed architecture on the JAAD, ETH/UCY and Stanford Drone datasets reveal that the M2P3 system is significantly superior to selected state-of-the-art solutions.

Generating Bird’s Eye View from Egocentric RGB Videos

In this paper, we present a method for generating bird’s eye video from egocentric RGB videos. Working with egocentric views is tricky since such the view is highly warped and prone to occlusions. On the other hand, a bird’s eye view has a consistent scaling in at least the two dimensions it shows. Moreover, most of the state‐of‐the‐art systems for tasks such as path prediction are built for bird’s eye views of the subjects. We present a deep learning‐based approach that transfers the egocentric RGB images captured from a dashcam of a car to bird’s eye view. This is a task of view translation, and we perform two experiments. The first one uses an image‐to‐image translation method, and the other uses a video‐to‐video translation. We compare the results of our work with homographic transformation, and our SSIM values are better by a margin of 77% and 14.4%, and the RMSE errors are lower by 40% and 14.6% for image‐to‐image translation and video‐to‐video translation, respectively. We also visually show the efficacy and limitations of each method with helpful insights for future research. Compared to previous works that use homography and LIDAR for 3D point clouds, our work is more generalizable and does not require any expensive equipment.

Egocentric videoconferencing

We introduce a method for egocentric videoconferencing that enables hands-free video calls, for instance by people wearing smart glasses or other mixed-reality devices. Videoconferencing portrays valuable non-verbal communication and face expression cues, but usually requires a front-facing camera. Using a frontal camera in a hands-free setting when a person is on the move is impractical. Even holding a mobile phone camera in the front of the face while sitting for a long duration is not convenient. To overcome these issues, we propose a low-cost wearable egocentric camera setup that can be integrated into smart glasses. Our goal is to mimic a classical video call, and therefore, we transform the egocentric perspective of this camera into a front facing video. To this end, we employ a conditional generative adversarial neural network that learns a transition from the highly distorted egocentric views to frontal views common in videoconferencing. Our approach learns to transfer expression details directly from the egocentric view without using a complex intermediate parametric expressions model, as it is used by related face reenactment methods. We successfully handle subtle expressions, not easily captured by parametric blendshape-based solutions, e.g., tongue movement, eye movements, eye blinking, strong expressions and depth varying movements. To get control over the rigid head movements in the target view, we condition the generator on synthetic renderings of a moving neutral face. This allows us to synthesis results at different head poses. Our technique produces temporally smooth video-realistic renderings in real-time using a video-to-video translation network in conjunction with a temporal discriminator. We demonstrate the improved capabilities of our technique by comparing against related state-of-the art approaches.

North in the head: spatial reference frame and map orientation

North in the head: spatial reference frame and map orientation

An Ego-Vision System for Discovering Human Joint Attention

Joint attention often happens during social interactions, in which individuals share focus on the same object. This article proposes an egocentric vision-based system (ego-vision system) that aims to discover the objects looked at jointly by a group of persons engaged in interactive activities. The proposed system relies on a collection of wearable eye-tracking cameras that provide an egocentric view of the interaction scenes as well as points-of-gaze measurement of each participant. Technically in our system, we develop a hierarchical conditional random field (CRF) based graphical model that can temporally localize joint attention periods and spatially segment objects of joint attention. By solving these two coupled tasks together in an iterative optimization procedure, we show that human joint attention can be reliably discovered from videos even with cluttered background and noisy gaze measurement. A new dataset of joint attention is collected and annotated for evaluating the two tasks of joint attention where two to four persons are involved. Experimental results demonstrate that our approach achieves state-of-the-art performance on both tasks of spatial segmentation and temporal localization of joint attention.

The Historical Presidency: “No Man Is Big Enough”: President Harding's Defense of the Proto‐Modern Executive

AbstractWarren Harding's administration was not, as is widely perceived, a failed experiment in executive whiggism but a surprisingly spirited defense of presidential authority against the strongest anti‐executive backlash since Reconstruction. His support for active‐interventionist conservatism and a modified style of “steward” leadership separated him from his traditionalist Republican predecessors—William McKinley and William H. Taft—and successor—Calvin Coolidge. Ambitiously, Harding sought to fuse elements of the Roosevelt–Wilson experiments in presidential leadership with his own, less egocentric view of “balanced” constitutional government. His adaptability helped maintain the expansion of the administrative state and of the “institutional” and “rhetorical” aspects of presidential power, albeit at more modest rates than under Theodore Roosevelt and Woodrow Wilson. By initiating, or further enabling, growth trends in cabinet government, media influence, and institutional reform, he developed operational templates used by later Republican presidents, notably, Dwight D. Eisenhower and Ronald Reagan. Rather than being dismissed as irrelevant, therefore, Harding’s tenure should be credited both for its innovation and flexibility at a time of institutional crisis and for its contribution to the development of post‐1930s Republican presidential leadership.

Translational and Rotational Arrow Cues (TRAC) Navigation Method for Manual Alignment Tasks.

Many tasks in image-guided surgery require a clinician to manually position an instrument in space, with respect to a patient, with five or six degrees of freedom (DOF). Displaying the current and desired pose of the object on a 2D display such as a computer monitor is straightforward. However, providing guidance to accurately and rapidly navigate the object in 5-DOF or 6-DOF is challenging. Guidance is typically accomplished by showing distinct orthogonal viewpoints of the workspace, requiring simultaneous alignment in all views. Although such methods are commonly used, they can be quite unintuitive, and it can take a long time to perform an accurate 5-DOF or 6-DOF alignment task. In this article, we describe a method of visually communicating navigation instructions using translational and rotational arrow cues (TRAC) defined in an object-centric frame, while displaying a single principal view that approximates the human's egocentric view of the physical object. The target pose of the object is provided but typically is used only for the initial gross alignment. During the accurate-alignment stage, the user follows the unambiguous arrow commands. In a series of human-subject studies, we show that the TRAC method outperforms two common orthogonal-view methods-the triplanar display, and a sight-alignment method that closely approximates the Acrobot Navigation System-in terms of time to complete 5-DOF and 6-DOF navigation tasks. We also find that subjects can achieve 1 mm and 1° accuracy using the TRAC method with a median completion time of less than 20 seconds.

An Explanation of Why I Can’t Contribute to This Narrative

An Explanation of Why I Can’t Contribute to This Narrative John Gregg (bio) It has been one of the great, unexpected pleasures of the Western Flyer restoration process that I get to work with notable scientists and authors. When one of them graciously asked if I would write an explanation as to why I’m involved in this project, I said yes before I thought about it. I was caught up in the excitement of hanging out with this group, before the truth fought forward and reminded me why I wanted to decline this assignment. I can’t write. I have an incomplete education and am the very personification of the problem E. O. Wilson described in his 1998 book Consilience. Being schooled in things technical and precise, I lack almost completely a sufficient exposure to the humanities, social discourse, and artist-y things. I am, in fact, a perfect exemplar of the problem of separation of art and science. My co-contributors, on the other hand, are examples that exceptions are, and should be, made. They are not only skilled scientists and scholars, but also philosophical thinkers and lyrical writers. Our societal house has long been too divided between art and science. Artistic creativity has not been rewarded in the scientific milieu, and precision and validation are rarely valued in art. It has been said (not by me—I was doing math) that Mona Lisa’s smile came not from divine intervention per Giorgio Vasari, but as the result of da Vinci’s exacting and painstaking anatomical research—so he was an early exception, blending scientific curiosity with artistic brilliance. But, alas, there are few da Vincis. In seeking a career, we are asked to separate into two camps, like a schoolyard game of Red Rover. In choosing the technical path, we are made to abandon the personal and egocentric view of things in favor of an ordered set of impersonal and dogmatic constructs. The artists, conversely, are told to abandon the group view and interpret the world through their own eyes and sensibilities. For the artist, the ordered [End Page 490] process is anathema, and group approval and peer review the road to ruin. Science has sought to suppress spurious creativity in its formulation. It rewards repetition and exactness, while art specifically frowns on anything that appears derivative. And yet, Piero Scaruffi noticed that all major scientific revolutions have coincided with a climate of creativity. This fact was also graphed by Ed Ricketts on butcher paper hung on the walls of his lab. He (without Google or Wikipedia) mapped all major events in human history throughout the world and noticed that similar patterns emerge in countries far removed from one another. Periods of great advances in both art and science were followed by long periods of quiescence—that was true in Japan and in Europe. This is what Stephen Jay Gould and Niles Eldredge called “punctuated equilibrium” in 1972. This is contrasted against phyletic gradualism—the idea that evolution occurs as a slow and steady transformation. One of these rapid steps forward seems to have occurred on the Western Flyer in 1940. For Steinbeck and Ricketts—an artist and a scientist—the dichotomy of art and science could be bridged by immersive observation. That is what Ricketts and Steinbeck did on their 6-week voyage. They observed. Deeply. The science that followed gave truths about nature. The art generated a series of notable books and essays. By listening to ribald stories from the crew about life in Monterey (and from living “down on his soles” in the 1930s on the “Row”), Steinbeck—a notorious “lint picker” of facts—penned Cannery Row and Sweet Thursday. Many nights during the voyage, Steinbeck huddled close to the Flyer’s shortwave radio and listened to the unfolding drama of the Nazi invasion of Norway and the brave resistance that became the material for The Moon Is Down. Their self-described Homeric journey also included a story gathered in La Paz, which became The Pearl, as well as greater familiarity with Mexican history and culture—which may have inspired Steinbeck to write the film scripts for Viva Zapata, and The Forgotten Village...

Recommending Influenceable Targets Based on Influence Propagation Through Activity Behaviors in Online Social Media

Online Social Media (OSM) is a platform through which the users present themselves to the connected world by means of messaging, posting, reacting, tagging, and sharing on different contents with also other social activities. Nowadays, it has a vast impact on various aspects of the industry, business and society along with on user’s life. In an OSN platform, reaching the target users is one of the primary focus for most of the businesses and other organizations. Identification and recommendation of influenceable targets help to capture the appropriate audience efficiently and effectively. In this paper, an effective model has been discussed in egocentric OSN by incorporating an efficient influence measured Recommendation System in order to generate a list of top most influenceable target users among all connected network members for any specific social network user. Firstly the list of interacted network members has been updated based on all activities. On which the interacted network members with most similar activities have been recommended based on the specific influence category with sentiment type. After that, the topmost influenceable network members in the basis of the required amount among those updated list of interacted network members have been identified with proper ranking by analyzing the similarity and frequency of their activity contents with respect to the activity contents of the main user. Through these two continuous stages, an effective list of top influenceable targets of the main user has been distinguished from the egocentric view of any social network.