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Selected Papers from the ISPRS Tracking and Imaging Challenge 2014

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (15 January 2015) | Viewed by 38906

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Special Issue Editors

Prof. Dr. Stephan Winter

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Guest Editor

Department of Infrastructure Engineering, The University of Melbourne, Victoria 3010, Australia
Interests: wayfinding and navigation; intelligent transport systems; geographic information science
Special Issues, Collections and Topics in MDPI journals

Dr. Alper Yilmaz

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Guest Editor

Photogrammetric Computer Vision Lab, Ohio State University, Columbus, OH, USA
Interests: image understanding; photogrammetry

Prof. Dr. Monika Sester

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Guest Editor

Institute of Cartography and Geoinformatics, Leibniz University Hannover, Welfengarten 1, 30167 Hannover, Germany
Interests: multiscale data; data integration; trajectory mining; traffic data analysis; integrated analysis of VGI data; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ISPRS Tracking and Imaging Challenge 2014 (TIC’14) has inspired research amongst academic communities such as computer vision, photogrammetry, spatial computing, robotics, and GIScience. With TIC’14 behind us, we are now openly inviting papers adhering to the TIC’14 specifications for the Special Issue of the ISPRS International Journal of Geo-Information.

TIC’14 required that two types of data are linked in innovative ways, namely trajectory data, as it is captured, for example, from mobile positioning sensors, smart cards, or e-tags, and image data as it is captured, for example, from tourist photos on Flickr, smartphones, CCTV, or car mounted cameras. You may even think of wearable computing. Only two conditions were imposed:

The presented solution must fundamentally require both types of data, i.e., cannot be realized with one data set alone.
The set task must demonstrate strong and novel benefits from integrating these two data sets.

The combination of the data sets can happen at any level of processing, from sensing through analysis to communicating derived information. Applications can be chosen in any field, for example (but without preference) urban informatics, transportation research, natural environment observation and management, or marine applications. They could aim, for example (again, without preference), to track individuals, vehicles, or crowds in physical or social environments, to analyze movement or activity patterns or densities, or to communicate observed dynamics of movement or activities. Goals could be, for example:

To close the semantic gap between trajectories and movement explanation.
To analyze human social behavior or environmental response while moving.
To interpret trajectories from image sequences.
To communicate routes by pictures.
To analyze changes in the environment.

Details of TIC’14 can be found at http://www2.isprs.org/commissions/comm2/wg8/tic.html.

Paper submissions for the special issue must be received by 15 January 2015. All instructions can be found at https://0-www-mdpi-com.brum.beds.ac.uk/journal/ijgi/special_issues/tracking_imaging. Please note that the journal is an open access journal, however, no processing fees will be charged for this special issue.

Open Call-for-Paper: https://0-www-mdpi-com.brum.beds.ac.uk/files/si/ijgi_tic_open_cfp.pdf

Prof. Stephan Winter
Dr. Alper Yilmaz
Prof. Monika Sester
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

ISPRS challenge
trajectories
tracking
sensor data
mobility

Published Papers (5 papers)

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Research

1192 KiB

Open AccessArticle

GPS-Aided Video Tracking

by Udo Feuerhake, Claus Brenner and Monika Sester

ISPRS Int. J. Geo-Inf. 2015, 4(3), 1317-1335; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031317 - 06 Aug 2015

Cited by 2 | Viewed by 5691

Abstract

Tracking moving objects is both challenging and important for a large variety of applications. Different technologies based on the global positioning system (GPS) and video or radio data are used to obtain the trajectories of the observed objects. However, in some use cases, they fail to provide sufficiently accurate, complete and correct data at the same time. In this work we present an approach for fusing GPS- and video-based tracking in order to exploit their individual advantages. In this way we aim to combine the reliability of GPS tracking with the high geometric accuracy of camera detection. For the fusion of the movement data provided by the different devices we use a hidden Markov model (HMM) formulation and the Viterbi algorithm to extract the most probable trajectories. In three experiments, we show that our approach is able to deal with challenging situations like occlusions or objects which are temporarily outside the monitored area. The results show the desired increase in terms of accuracy, completeness and correctness. Full article

(This article belongs to the Special Issue Selected Papers from the ISPRS Tracking and Imaging Challenge 2014)

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629 KiB

Open AccessArticle

Tracking 3D Moving Objects Based on GPS/IMU Navigation Solution, Laser Scanner Point Cloud and GIS Data

by Siavash Hosseinyalamdary, Yashar Balazadegan and Charles Toth

ISPRS Int. J. Geo-Inf. 2015, 4(3), 1301-1316; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031301 - 31 Jul 2015

Cited by 24 | Viewed by 9960

Abstract

Monitoring vehicular road traffic is a key component of any autonomous driving platform. Detecting moving objects, and tracking them, is crucial to navigating around objects and predicting their locations and trajectories. Laser sensors provide an excellent observation of the area around vehicles, but the point cloud of objects may be noisy, occluded, and prone to different errors. Consequently, object tracking is an open problem, especially for low-quality point clouds. This paper describes a pipeline to integrate various sensor data and prior information, such as a Geospatial Information System (GIS) map, to segment and track moving objects in a scene. We show that even a low-quality GIS map, such as OpenStreetMap (OSM), can improve the tracking accuracy, as well as decrease processing time. A bank of Kalman filters is used to track moving objects in a scene. In addition, we apply non-holonomic constraint to provide a better orientation estimation of moving objects. The results show that moving objects can be correctly detected, and accurately tracked, over time, based on modest quality Light Detection And Ranging (LiDAR) data, a coarse GIS map, and a fairly accurate Global Positioning System (GPS) and Inertial Measurement Unit (IMU) navigation solution. Full article

(This article belongs to the Special Issue Selected Papers from the ISPRS Tracking and Imaging Challenge 2014)

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1196 KiB

Open AccessArticle

MAARGHA: A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data

by Deepak Rajamohan, Bhavana Gannu and Krishnan Sundara Rajan

ISPRS Int. J. Geo-Inf. 2015, 4(3), 1225-1245; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4031225 - 22 Jul 2015

Cited by 22 | Viewed by 7445

Abstract

Road infrastructure in countries like India is expanding at a rapid pace and is becoming increasingly difficult for authorities to identify and fix the bad roads in time. Current Geographical Information Systems (GIS) lack information about on-road features like road surface type, speed breakers and dynamic attribute data like the road quality. Hence there is a need to build road monitoring systems capable of collecting such information periodically. Limitations of satellite imagery with respect to the resolution and availability, makes road monitoring primarily an on-field activity. Monitoring is currently performed using special vehicles that are fitted with expensive laser scanners and need skilled resource besides providing only very low coverage. Hence such systems are not suitable for continuous road monitoring. Cheaper alternative systems using sensors like accelerometer and GPS (Global Positioning System) exists but they are not equipped to achieve higher information levels. This paper presents a prototype system MAARGHA (MAARGHA in Sanskrit language means an eternal path to solution), which demonstrates that it can overcome the disadvantages of the existing systems by fusing multi-sensory data like camera image, accelerometer data and GPS trajectory at an information level, apart from providing additional road information like road surface type. MAARGHA has been tested across different road conditions and sensor data characteristics to assess its potential applications in real world scenarios. The developed system achieves higher information levels when compared to state of the art road condition estimation systems like Roadroid. The system performance in road surface type classification is dependent on the local environmental conditions at the time of imaging. In our study, the road surface type classification accuracy reached 100% for datasets with near ideal environmental conditions and dropped down to 60% for datasets with shadows and obstacles. Full article

(This article belongs to the Special Issue Selected Papers from the ISPRS Tracking and Imaging Challenge 2014)

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1475 KiB

Open AccessArticle

Routing in Dense Human Crowds Using Smartphone Movement Data and Optical Aerial Imagery

by Florian Hillen, Oliver Meynberg and Bernhard Höfle

ISPRS Int. J. Geo-Inf. 2015, 4(2), 974-988; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4020974 - 12 Jun 2015

Cited by 4 | Viewed by 9284

Abstract

In this paper, we propose a navigation approach for smartphones that enables visitors of major events to avoid crowded areas or narrow streets and to navigate out of dense crowds quickly. Two types of sensor data are integrated. Real-time optical images acquired and transmitted by an airborne camera system are used to compute an estimation of a crowd density map. For this purpose, a patch-based approach with a Gabor filter bank for texture classification in combination with an interest point detector and a smoothing function is applied. Furthermore, the crowd density is estimated based on location and movement speed of in situ smartphone measurements. This information allows for the enhancement of the overall crowd density layer. The composed density information is input to a least-cost routing workflow. Two possible use cases are presented, namely (i) an emergency application and (ii) a basic routing application. A prototypical implementation of the system is conducted as proof of concept. Our approach is capable of increasing the security level for major events. Visitors are able to avoid dense crowds by routing around them, while security and rescue forces are able to find the fastest way into the crowd. Full article

(This article belongs to the Special Issue Selected Papers from the ISPRS Tracking and Imaging Challenge 2014)

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2342 KiB

Open AccessArticle

Real-Time Sidewalk Slope Calculation through Integration of GPS Trajectory and Image Data to Assist People with Disabilities in Navigation

by Yihan Lu and Hassan. A. Karimi

ISPRS Int. J. Geo-Inf. 2015, 4(2), 741-753; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi4020741 - 04 May 2015

Cited by 7 | Viewed by 5705

Abstract

People with disabilities face many obstacles in everyday outdoor travels. One of the most notable obstacles is steep slope on sidewalk segments. Current navigation systems/services do not all support map databases with slope attributes and cannot calculate sidewalk slope in real time. In this paper, we present a technique for calculating slopes of sidewalk segments by image data and predict the most suitable route for each individual user through integration with GPS trajectory. In our technique we make use of GPS trajectory data, to identify the sidewalk segment on which the traveler will most probably pass, and images of the identified sidewalk segment. Through edge detection techniques we detect edges of objects, such as buildings, billboards, and walls, in the background. Slope of the segment is then calculated by comparing its line representation in the map with the detected edges. Our experiment result indicates effective calculation of sidewalk slopes. Full article

(This article belongs to the Special Issue Selected Papers from the ISPRS Tracking and Imaging Challenge 2014)

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