Chadly Marouane

Indoor positioning using smartphone camera

With the increasing computational power of mobile devices and the increase in the usage of ordinary location-based services, the area of indoor location-based services is of growing interest. Nowadays indoor location-based services are used mainly for personalized information retrieval of maps and points of interest. Advanced location-based functionality often suffers from imprecise positioning methods. In this paper we present a simple, yet powerful positioning method inside buildings which allows for a fine-grained detection of the position and orientation of a user while being easy to deploy and optimize. The main contribution of this paper consists of the combination of an image recognition system with a distance estimation algorithm to gain a high-quality positioning service independent from any infrastructure using the camera of a mobile device. Moreover this type of positioning can be operated in a user-contributed way and is less susceptible to small changes in the environment as compared to popular WLAN-based systems. As an extension, we propose the usage of very coarse WLAN positioning to reduce the size of the candidate set of image recognition and hence speed up the system.

Free-hand pointing for identification and interaction with distant objects

In this paper, we investigate pointing as a lightweight form of gestural interaction in cars. In a pre-study, we show the technical feasibility of reliable pointing detection with a depth camera by achieving a recognition rate of 96% in the lab. In a subsequent in-situ study, we let drivers point to objects inside and outside of the car while driving through a city. In three usage scenarios, we studied how this influenced their driving objectively, as well as subjectively. Distraction from the driving task was compensated by a regulation of driving speed and did not have a negative influence on driving behaviour. Our participants considered pointing a desirable interaction technique in comparison to current controller-based interaction and identified a number of additional promising use cases for pointing in the car.

Visual positioning systems — An extension to MoVIPS

Due to the increasing popularity of location-based services, the need for reliable and cost-effective indoor positioning methods is rising. As an alternative to radio-based localization methods, in 2011, we introduced MoVIPS (Mobile Visual Indoor Positioning System), which is based on the idea to extract visual feature points from a query image and compare them to those of previously collected geo-referenced images. The general feasibility of positioning by SURF points on a conventional smartphone was already shown in our previous work. However, the system still faced several shortcomings concerning real-world usage such as request times being too high and distance estimation being unreliable because of the employed estimation method not being rotation invariant. In this paper, three extensions are presented that improve the practical applicability of MoVIPS. To speed up request times, both a dead reckoning approach (based on step counting using the accelerometer) and an orientation estimation (based on the smartphones compass) are introduced to filter relevant images from the database and thus to reduce the amount of images to compare the query image to. Furthermore, the vectors of the SURF points are quantized. For this purpose, clusters are calculated from all SURF points from the database. As a result, each image can be represented by a histogram of cluster frequencies, which can be compared with each other a lot more efficiently. The third extension is an improvement of the distance estimation method, which uses the matched feature points of an image to perform a perspective transformation and to determine the actual position with the aid of the transformation matrix.

SensX: About sensing and assessment of complex human motion

The great success of wearables and smartphone apps for provision of extensive physical workout instructions boosts a whole industry dealing with consumer oriented sensors and sports equipment. But with these opportunities there are also new challenges emerging. The unregulated distribution of instructions about ambitious exercises enables unexperienced users to undertake demanding workouts without professional supervision which may lead to suboptimal training success or even serious injuries. We believe, that automated supervision and realtime feedback during a workout may help to solve these issues. Therefore we introduce four fundamental steps for complex human motion assessment and present SensX, a sensor-based architecture for monitoring, recording, and analyzing complex and multi-dimensional motion chains. We provide the results of our preliminary study encompassing 8 different body weight exercises, 20 participants, and more than 9,220 recorded exercise repetitions. Furthermore, insights into SensXs classification capabilities and the impact of specific sensor configurations onto the analysis process are given.

Visual odometry using motion vectors from visual feature points

In recent years, location-based services and indoor positioning systems gained increasing importance for both, research and industry. Visual localization systems have the advantage of not being dependent on dedicated infrastructure and thus are especially interesting for navigation within buildings. While there are already approaches of using pre-recorded databases of reference images to obtain an absolute position for a given query image, suitable means to estimate the relative movement of pedestrians from an ego perspective video are still missing. This paper presents a novel visual odometry system for pedestrians. The user carries a mobile device while walking - the camera aims into the direction of walking. Using only the video stream as input, the system generates a two-dimensional trajectory, which describes the path traveled by the user. Both, the users current heading as well as the walking direction are estimated based on the movement of visual feature points in successive video frames. In order to assess the accuracy of the system, it is evaluated in three different scenarios (indoors in an university building, in an urban area and in a city park). Not relying on reference points (for instance provided by a database, which references visual feature points with geo-data), the error accumulates with distance traveled. After a walked distance of 100 meters, the average error lies between 4.6 and 13.9 meters (depending on the scenario). Consequently, the system is a promising approach for visual odometry, which can be used in conjunction with existing absolute visual positioning systems or as a core part of a future SLAM (simultaneous localization and mapping) system.

Mobile Authentisierung im Unternehmensalltag

ZusammenfassungImmer mehr Anwendungen und digitale Dienste verlagern sich in die Cloud, insbesondere im Unternehmenskontext. Dadurch entstehen neue Anforderungen an die Sicherheit. Zwar bieten viele Systeme im Web ein detailliertes Sicherheitskonzept, der Zugang zu diesen Diensten wird jedoch meist durch ein einfaches Kennwort geschützt. Insbesondere im Unternehmensumfeld werden hohe Anforderungen an ein sicheres Passwort gestellt. Dies hat jedoch zur Folge, dass Passwörter schwieriger zu merken sind und deshalb viele Mitarbeiter Passwörter fahrlässig notieren, unverschlüsselt abspeichern oder mehrfach verwenden. Um diesen Herausforderungen gerecht zu werden, stellen wir ein Konzept zur sicheren Verwaltung von Zugangsberechtigungen im Unternehmensumfeld vor. Dabei soll das Konzept die Anforderungen bezüglich Sicherheit, Usability und Einsatz im Unternehmenskontext berücksichtigen.

An Open, Labeled Dataset for Analysis and Assessment of Human Motion

Analysis of human activity, e.g., by tracking and analyzing motion information or vital signs became lots of attention in medical as well as athletic appliances during the last years. Nonetheless, comprehensive and labeled datasets containing human motion information are only sparsely accessible to the public. Especially qualitatively labeled datasets are rare, although they are of great value for the development of concepts concerning qualitative motion assessment, e.g., to avoid injuries during athletic workouts or to optimize a training’s success.

Automated Recognition and Difficulty Assessment of Boulder Routes

Due to fast distribution of powerful, portable processing devices and wearables, the development of learning-based IoT-applications for athletic or medical usage is accelerated. But besides the offering of quantitative features, such as counting repetitions or distances, there are only a few systems which provide qualitative services, e.g., detecting malpositions to avoid injuries or to optimize training success.

Visual Odometry for Pedestrians Based on Orientation Attributes of SURF

With the decreasing size and prize of cameras, visual positioning systems for persons are becoming more attractive and feasible. A main advantage of visual methods is that they can be independent of any infrastructure and are therefore applicable in indoor as well as outdoor scenarios. As such, they are an attractive alternative to infrastructure based methods. This paper presents a method that uses visual data to create a two-dimensional trajectory of the pedestrians movement. A camera that is mounted on the persons upper body is used to obtain the image data. With the SURF algorithm, feature points that posses specific attributes are extracted from the image frames. Based on these attributes, the method determines the pedestrians steps and estimates the heading at each step. As each determination of a new position is based on previous estimations, the method accumulates errors with increasing distances. An extensive evaluation with different test persons for various scenarios demonstrates that the method achieves a reasonably good overall accuracy for shorter distances. For distances of up to 25 m, a mean error of 5.52 m for indoor scenarios and of 7.56 m for outdoor scenarios has been determined. Furthermore, the method is also reliably functional at increasing walking and running speeds. An additional evaluation shows the usability of one of the SURF-attributes for the implementation of an activity detector for different movement speeds. The method robustly detects steps with a high accuracy at an error rate of approximately one percent. However, just like other Pedestrian Dead Reckoning methods, the heading estimation proves to be challenging and to be a source of errors.

Step and activity detection based on the orientation and scale attributes of the SURF algorithm

In recent years, the importance of location-based services and indoor positioning systems increased significantly for both, research and industry. Visual localization systems have the advantage of not depending on dedicated infrastructure and thus they are interesting for navigation within buildings. While there are already approaches which are using pre-recorded databases of reference images to obtain an absolute position for a given query image, suitable applications which are estimating the relative movement of pedestrians out of a first person perspective video are still missing. This paper presents a novel approach for a pedometer as well as for an activity detector using a such a first person perspective video stream of a pedestrian as input data. The system counts the number of steps and furthermore detects current activities of a user. Therefore, we analyze all video input data with the SURF algorithm in order to extract robust feature points. Especially the orientation and scaling properties of this feature points are used for an accurate measurement.