A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

Visual tracking of small animals in cluttered natural environments using a freely moving camera

Image-based tracking of animals in their natural habitats can provide rich behavioural data, but is very challenging due to complex and dynamic background and target appearances. We present an effective method to recover the positions of terrestrial animals in cluttered environments from video sequences filmed using a freely moving monocular camera. The method uses residual motion cues to detect the targets and is thus robust to different lighting conditions and requires no a-priori appearance model of the animal or environment. The detection is globally optimised based on an inference problem formulation using factor graphs. This handles ambiguities such as occlusions and intersections and provides automatic initialisation. Furthermore, this formulation allows a seamless integration of occasional user input for the most difficult situations, so that the effect of a few manual position estimates are smoothly distributed over long sequences. Testing our system against a benchmark dataset featuring small targets in natural scenes, we obtain 96% accuracy for fully automated tracking. We also demonstrate reliable tracking in a new data set that includes different targets (insects, vertebrates or artificial objects) in a variety of environments (desert, jungle, meadows, urban) using different imaging devices (day / night vision cameras, smart phones) and modalities (stationary, hand-held, drone operated)

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about invading antigens and needed antibodies. This information is encoded and sorted by T- and B-cells. The immune system has an adaptive component that can accelerate and intensify the immune response upon subsequent infection with the same antigen. The artificial memory cells attempt to mimic these characteristics for estimation error compensation and are constructed under normal conditions when all sensor systems function accurately, including those providing vehicle position and velocity information. The artificial memory cells consist of two main components: a collection of instantaneous measurements of relevant vehicle features representing the antigen and a set of instantaneous estimation errors or correction features, representing the antibodies. The antigen characterizes the dynamics of the system and is assumed to be correlated with the required corrections of position and velocity estimation or antibodies. When the navigation source is unavailable, the currently measured vehicle features from the onboard sensors are matched against the AIS antigens and the corresponding corrections are extracted and used to adjust the position and velocity estimation algorithm and provide the corrected estimation as actual measurement feedback to the vehicle’s control system. The proposed framework is implemented and tested through simulation in two versions: with corrections applied to the output or the input of the estimation scheme. For both approaches, the vehicle feature or antigen sets include increments of body axes components of acceleration and angular rate. The correction feature or antibody sets include vehicle position and velocity and vehicle acceleration adjustments, respectively. The impact on the performance of the proposed methodology produced by essential elements such as path generation method, matching algorithm, feature set, and the IMU grade was investigated. The findings demonstrated that in all cases, the proposed methodology could significantly reduce the accumulation of dead reckoning errors and can become a viable solution in situations where direct accurate measurements and other sources of information are not available. The functionality of the proposed methodology and its promising outcomes were successfully illustrated using the West Virginia University unmanned aerial system simulation environment

Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects

These are the Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

Detecting, segmenting and tracking bio-medical objects

Studying the behavior patterns of biomedical objects helps scientists understand the underlying mechanisms. With computer vision techniques, automated monitoring can be implemented for efficient and effective analysis in biomedical studies. Promising applications have been carried out in various research topics, including insect group monitoring, malignant cell detection and segmentation, human organ segmentation and nano-particle tracking. In general, applications of computer vision techniques in monitoring biomedical objects include the following stages: detection, segmentation and tracking. Challenges in each stage will potentially lead to unsatisfactory results of automated monitoring. These challenges include different foreground-background contrast, fast motion blur, clutter, object overlap and etc. In this thesis, we investigate the challenges in each stage, and we propose novel solutions with computer vision methods to overcome these challenges and help automatically monitor biomedical objects with high accuracy in different cases --Abstract, page iii

Analysis of Motion of People by a Stationary Camera

Hlavním cílem této práce bylo navrhnout a vytvořit systém sledování osob s aplikací v oboru bezpečnosti nebo pro analýzu chování zákazníka v obchodě. Systém byl úspěšně implementován pomocí metod KLT trekování, AdaBoost klasifikátoru a datové asociace pomocí Markovských řetězců a metody Monte Carlo. Implementace umožňuje analýzu pohybu lidí ve vnitřních i vnějších prostorech.The main goal of this thesis is to develop multi-target tracking system for use in field of security surveillance or for customer behavior analysis. The system was successfully implemented using KLT tracking, AdaBoost classifier and Markov Chain Monte Carlo data association. It is able to perform analysis of motion of people in both outdoor and indoor environment.

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available