Human Tracking Research Papers - Academia.edu (original) (raw)

Industrial growth has increased the number of jobs hence increase the number of employees. Therefore, it is impossible to track the location of all employees in the same building at the same time as they are placed in a different... more

Industrial growth has increased the number of jobs hence increase the number of employees. Therefore, it is impossible to track the location of all employees in the same building at the same time as they are placed in a different department. In this work, a real-time indoor human tracking system is developed to determine the location of employees in a real-time implementation. In this work, the long-range (LoRa) technology is used as the communication medium to establish the communication between the tracker and the gateway in the developed system due to its low power with high coverage range besides requires low cost for deployment. The received signal strength indicator (RSSI) based positioning method is used to measure the power level at the receiver which is the gateway to determine the location of the employees. Different scenarios have been considered to evaluate the performance of the developed system in terms of precision and reliability. This includes the size of the area, the number of obstacles in the considered area, and the height of the tracker and the gateway. A real-time testbed implementation has been conducted to evaluate the performance of the developed system and the results show that the system has high precision and are reliable for all considered scenarios.

Malaria is the deadliest disease in the earth and a big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under... more

Malaria is the deadliest disease in the earth and a big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. This process is inefficient and the diagnosis depends on the experience and well knowledgeable person needed for the examination. Deep Learning algorithms have been applied to malaria blood smears for diagnosis before. However, practical performance has not been sufficient so far. This paper proposes a new and highly robust machine learning model based on a Convolutional Neural Network (CNN) which automatically classifies and predicts infected cells in thin blood smears on standard microscope slides. We used the tenfold cross-validation layer of convolutional neural network on 27,558 single-cell images to understand the parameter of the cell. We made three types of CNN models to compare the accuracy and select the precise accurate-Basic CNN, VGG-19 Frozen CNN and VGG-19 Fine Tuned CNN. Then by comparing the accuracy of the three models, we found the model with higher rate of accuracy.

This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover, it... more

This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover, it works with infra-red on and infra-red off. The method consists of five parts – training image acquisition, background subtraction, feature extraction, system training, and system testing. Firstly, some sample video clips have been taken with an IP camera for initial system implementation. The clips are then filtered to separate background and foreground. After that, some morphological operations are carried out to identify the most significant motion in the foreground. Those parts are cropped with some extra area and used to train a multiclass support vector machine (SVM) along with an image subset of the people detection dataset of The National Institute for Research in Computer Science and Control (French: Institut National de Recherche en Inform...

The precise localization of human operators in robotic workplaces is an important requirement to be satisfied in order to develop human-robot interaction tasks. Human tracking provides not only safety for human operators, but also context... more

The precise localization of human operators in robotic workplaces is an important requirement to be satisfied in order to develop human-robot interaction tasks. Human tracking provides not only safety for human operators, but also context information for ...

—This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover,... more

—This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover, it works with infra-red on and infra-red off. The method consists of five parts – training image acquisition, background subtraction, feature extraction, system training, and system testing. Firstly, some sample video clips have been taken with an IP camera for initial system implementation. The clips are then filtered to separate background and foreground. After that, some morphological operations are carried out to identify the most significant motion in the foreground. Those parts are cropped with some extra area and used to train a multiclass support vector machine (SVM) along with an image subset of the people detection dataset of The A total of 597 images have been used as positive images and a total of 662 images have been used as negative images. Average detection accuracy of the system without infra-red is 89.37% and average detection accuracy of the system with infra-red is 72.66%. Therefore the average detection accuracy is 81.1%. We conclude (using dependent probabilistic analysis) that our system performs on an average of 89.37% accuracy based on our frame based analysis of video feeds.

Multiple cameras are needed to cover large environments for monitoring activity. To track people successfully in multiple perspective imagery, one needs to establish correspondence between objects captured in multiple cameras. We present... more

Multiple cameras are needed to cover large environments for monitoring activity. To track people successfully in multiple perspective imagery, one needs to establish correspondence between objects captured in multiple cameras. We present a system for tracking people in multiple uncalibrated cameras. The system is able to discover spatial relationships between the camera fields of view and use this information to correspond between different perspective views of the same person. We employ the novel approach ...

While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present... more

While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present data obtained using a hardware system that is able to capture synchronized video and ground-truth 3D motion. The resulting HUMANEVA datasets contain multiple

In this paper, a vision system for safety applications in human-robot collaboration is presented. The system is based on two Time-Of-Flight (TOF) cameras for 3D acquisition. The point clouds are registered in a common reference system,... more

In this paper, a vision system for safety applications in human-robot collaboration is presented. The system is based on two Time-Of-Flight (TOF) cameras for 3D acquisition. The point clouds are registered in a common reference system, and human and robot recognition are then implemented. Human recognition is performed using a customized version of the Histogram of Oriented Gradient (HOG) algorithm. Robot recognition is achieved using a procedure based on the Kanade-Lucas-Tomasi (KLT) algorithm. Two safety strategies have been developed. The first one is based on the definition of suitable comfort zones of both the operator and the robot; the second implements virtual barriers between the operator and the robot. The vision system has been characterized in terms of (i) human and robot recognition performance, (ii) correctness of the detection of safety situations and (iii) evaluation of the time delays in the detection. The results show that the human operator is robustly recognized provided that he moves frontally with respect to the TOF cameras and the robot is always recognized. The safety situations are always identified correctly with an average time delay of 0.86 ± 0.63 s (k=1).

The Bayesian occupancy filter (BOF) (1) has achieved promising results in the object tracking applications. This paper presents a new development of BOF which inherits original BOF's advantages in handling occlusion and... more

The Bayesian occupancy filter (BOF) (1) has achieved promising results in the object tracking applications. This paper presents a new development of BOF which inherits original BOF's advantages in handling occlusion and representing objects' shape. Meanwhile, the new formulation has significantly reduced original BOF's complexities and can be run in realtime. In Bayesian occupancy filter, the environment is finely divided

Detecting, localizing and tracking humans within an industrial environment are three tasks which are of central importance towards achieving automation in workplaces and intelligent environments. This is because unobtrusive, real-time and... more

Detecting, localizing and tracking humans within an industrial environment are three tasks which are of central importance towards achieving automation in workplaces and intelligent environments. This is because unobtrusive, real-time and reliable person tracking provides valuable input to solving problems such as workplace surveillance and event/activity recognition and, also, contributes to safety and optimized use of resources. This paper presents a passive approach to the problem of person tracking that is based on a network of conventional color cameras. The proposed approach exhibits robustness to challenging conditions that are encountered in industrial environments due to illumination artifacts, occlusions and the highly dynamic nature of the observed scenes. The multiple views of the environment that the system employs are used to obtain a volumetric representation of the humans within it, in real-time. Although human tracking can be achieved based solely on such a volumetric representation, in demanding scenes, this information is not enough to recover from tracking failures. Thus, in this work, we collect and update a representation of the color appearance of the persons in the environment. The combination of volumetric and color information reinforces tracking robustness, even when a person is not visible by any of the cameras for extended time intervals. The proposed approach has been extensively evaluated in comparison with an existing state of the art method and pertinent results are reported.

This paper presents a real-time vision-based system to assist a person with dementia wash their hands. The system uses only video inputs, and assistance is given as either verbal or visual prompts, or through the enlistment of a human... more

This paper presents a real-time vision-based system to assist a person with dementia wash their hands. The system uses only video inputs, and assistance is given as either verbal or visual prompts, or through the enlistment of a human caregiver’s help. The system combines a Bayesian sequential estimation framework for tracking hands and towel, with a decision theoretic framework for computing policies of action. The decision making system is a partially observable Markov decision process, or POMDP. Decision policies dictating system actions are computed in the POMDP using a point-based approximate solution technique. The tracking and decision making systems are coupled using a heuristic method for temporally segmenting the input video stream based on the continuity of the belief state. A key element of the system is the ability to estimate and adapt to user psychological states, such as awareness and responsiveness. We evaluate the system in three ways. First, we evaluate the hand-t...

This article presents a distributed agent-based system that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent environment for... more

This article presents a distributed agent-based system that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent environment for location and ...

ABSTRACT Human detection and tracking is a primary focus for visual surveillance systems. However, current systems for human tracking are often complex and require vast amounts of computing resources. A reduction in complexity of the... more

ABSTRACT Human detection and tracking is a primary focus for visual surveillance systems. However, current systems for human tracking are often complex and require vast amounts of computing resources. A reduction in complexity of the overall tracking system can be achieved by using a System of Systems (SoS) architecture. Once formed, a SoS architecture must achieve desirable levels of interoperability and integration. Proposed in this paper is a human tracking system that uses a net-centric SoS architecture capable of detecting a human via video surveillance and subsequently providing coordinates for mobile robot tracking. This SoS architecture establishes integration and conceptual interoperability through the standardization of communication and data representation. Within the SoS, communication is accomplished using a client-server architecture while data is structured according to a custom XML framework. The resulting SoS is composed of decentralized independent systems inter-operating on heterogeneous platforms to achieve the ultimate goal of human tracking.

In this paper we present a novel method called Temporal Nearest End-Effectors (TNEE) to automatically classify full-body human actions captured in real-time. This method uses a simple representation for modeling actions based exclusively... more

In this paper we present a novel method called Temporal Nearest End-Effectors (TNEE) to automatically classify full-body human actions captured in real-time. This method uses a simple representation for modeling actions based exclusively on the recent positions of ...

In this paper, an algorithm for human head detection over a distance exceeding 2.5 m between a camera and an object is described. This algorithm is used for the control of a robot, which has the additional limits of a moving camera,... more

In this paper, an algorithm for human head detection over a distance exceeding 2.5 m between a camera and an object is described. This algorithm is used for the control of a robot, which has the additional limits of a moving camera, moving objects, various face orientations, and unfixed illuminations. With these circumstances, only the assumption that human head and

We introduce the Hierarchical Partitioned Particle Filter (HPPF) designed specifically for articulated human track- ing. The HPPF is motivated by the hierarchical dependency between the human body parameters and the partial inde- pendence... more

We introduce the Hierarchical Partitioned Particle Filter (HPPF) designed specifically for articulated human track- ing. The HPPF is motivated by the hierarchical dependency between the human body parameters and the partial inde- pendence between certain of those parameters. The track- ing is model based and follows the analysis by synthesis principle. The limited information of the video sequence is balanced

While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present... more

While research on articulated human motion and pose estimation has progressed rapidly in the last few years, there has been no systematic quantitative evaluation of competing methods to establish the current state of the art. We present data obtained using a hardware system that is able to capture synchronized video and ground-truth 3D motion. The resulting HUMANEVA datasets contain multiple

Abstract This article presents a distributed agent-based system that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent... more

Abstract This article presents a distributed agent-based system that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent environment for location and ...

We present a method to solve the human silhouette tracking problem using 18 major human points. We used: a simple 2D model for the human silhouette, a linear prediction technique for initializing major points search, geometry... more

We present a method to solve the human silhouette tracking problem using 18 major human points. We used: a simple 2D model for the human silhouette, a linear prediction technique for initializing major points search, geometry anthropometric constraints for determining the search area and color measures for matching human body parts. In addition, we propose a method to solve the problem of human members recognition and 18 major human points detection using silhouette. This result can be used to initialize a human tracking algorithm for real time applications. Our main purpose is to develop a low computation cost algorithm, which can be used independently of camera motion. The output of the tracking algorithm is the position of 18 major human points and a 2D human body extraction. In cases of low quality imaging conditions or low background contrast, the result may be worst. For these cases we defined an appropriate criterion concerning tracking ability.