SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

SeaVipers - Computer Vision and Inertial Position Reference Sensor System (CVIPRSS)

This work describes the design and development of an optical, Computer Vision (CV) based sensor for use as a Position Reference System (PRS) in Dynamic Positioning (DP). Using a combination of robotics and CV techniques, the sensor provides range and heading information to a selected reference object. The proposed optical system is superior to existing ones because it does not depend upon special reflectors nor does it require a lengthy set-up time. This system, the Computer Vision and Inertial Position Reference Sensor System (CVIPRSS, pronounced \nickname), combines a laser rangefinder, infrared camera, and a pan--tilt unit with the robust TLD (Tracking--Learning--Detection) object tracker. In this work, a \nickname ~prototype is evaluated, showing promising results as viable PRS with research, commercial, and industrial applications

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Multi-sensor fusion for human-robot interaction in crowded environments

For challenges associated with the ageing population, robot assistants are becoming a promising solution. Human-Robot Interaction (HRI) allows a robot to understand the intention of humans in an environment and react accordingly. This thesis proposes HRI techniques to facilitate the transition of robots from lab-based research to real-world environments. The HRI aspects addressed in this thesis are illustrated in the following scenario: an elderly person, engaged in conversation with friends, wishes to attract a robot's attention. This composite task consists of many problems. The robot must detect and track the subject in a crowded environment. To engage with the user, it must track their hand movement. Knowledge of the subject's gaze would ensure that the robot doesn't react to the wrong person. Understanding the subject's group participation would enable the robot to respect existing human-human interaction. Many existing solutions to these problems are too constrained for natural HRI in crowded environments. Some require initial calibration or static backgrounds. Others deal poorly with occlusions, illumination changes, or real-time operation requirements. This work proposes algorithms that fuse multiple sensors to remove these restrictions and increase the accuracy over the state-of-the-art. The main contributions of this thesis are: A hand and body detection method, with a probabilistic algorithm for their real-time association when multiple users and hands are detected in crowded environments; An RGB-D sensor-fusion hand tracker, which increases position and velocity accuracy by combining a depth-image based hand detector with Monte-Carlo updates using colour images; A sensor-fusion gaze estimation system, combining IR and depth cameras on a mobile robot to give better accuracy than traditional visual methods, without the constraints of traditional IR techniques; A group detection method, based on sociological concepts of static and dynamic interactions, which incorporates real-time gaze estimates to enhance detection accuracy.Open Acces

From Pillars to AI Technology-Based Forest Fire Protection Systems

The importance of forest environment in the perspective of the biodiversity as well as from the economic resources which forests enclose, is more than evident. Any threat posed to this critical component of the environment should be identified and attacked through the use of the most efficient available technological means. Early warning and immediate response to a fire event are critical in avoiding great environmental damages. Fire risk assessment, reliable detection and localization of fire as well as motion planning, constitute the most vital ingredients of a fire protection system. In this chapter, we review the evolution of the forest fire protection systems and emphasize on open issues and the improvements that can be achieved using artificial intelligence technology. We start our tour from the pillars which were for a long time period, the only possible method to oversee the forest fires. Then, we will proceed to the exploration of early AI systems and will end-up with nowadays systems that might receive multimodal data from satellites, optical and thermal sensors, smart phones and UAVs and use techniques that cover the spectrum from early signal processing algorithms to latest deep learning-based ones to achieving the ultimate goal

A practical multirobot localization system

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems

Remote Sensing of Earth Resources Using Manned Spacecraft

Many NASA and industry-sponsored studies during the past several years have concluded that both synoptic and continuous remote sensing of the earth environment from orbital spacecraft offer unique advantages over similar aircraft sensor configurations. Wider earth resource coverage, extended duration capability, repeated orbital traces over ground-truth sites are some of the more apparent advantages. Still, the problem of data management and experiment programming, specif ically data taking and on-line data evaluation,are significantly large and yet unresolved. One of the more promising techniques for • providing timely and expert data management, as \u27well as having the benefit of human visual surveillance and target-of-opportunity selection from orbit, involves the use of manned spacecraft. A crew observer/experimenter with appropriate optical aids and sensor displays may play the dual role of systems manager as well as experimenter/ evaluator. Of course, duties such as sensor aiming, preparation, replenishment, retrieval, maintenance and data transmission are adjuncts to the primary experimenter activities which make the approach even more attractive. This manned experimentation role has been extensively studied at TRW Systems in the context of earth resource orbital missions utilizing Apollo follow-on hardware (AAP) with short and long-term experiment carriers, Manned Orbiting Laboratory (MOL) Vehicles fitted with similar sensor payloads, and extended duration orbital workshops incorporating Saturn launch vehicle spent stages with appropriate man-rated laboratory areas. One particular program conducted during the past year was an in-house study of an earth resource payload for a MOL-type vehicle. The study objective was to determine the engineering feasibility of incorporating such a payload within the laboratory area of a MOL vehicle. The advantages of this approach are apparent in that the MOL has (or can have) an orbital duration of up to 3 months without logistic resupply providing the capability (depending on orbital inclination and altitude) of repeated coverage of earth several times within this 90-day period