Properties and Tracking of Movements

Transcription

1 Second Conference of Junior Researchers in Civil Engineering 6 Properties and Tracking of Movements Zoltan Koppanyi BME Department of Photogrammetry and Geoinformatics, zoltan.koppanyi@fmt.bme.hu Abstract This paper discusses the tracking techniques that can provide data for analysing the movements of humans, animals, vehicles. In the paper I classify the movements by their properties. These properties are the degree of freedom, coverage, number of objects and time independent property of movements. This classification can be useful for choosing the appropriate tracking method. Afterwards the tracking method will be presented from the movement s point of view. These methods are based on different technologies, such as GPS, mobile communication network, Wi-Fi, Bluetooth, ultra-wide band, and some others. Introduction Nowadays understanding the behaviours and/or tracking movements of humans, animals, vehicles and other (this kind of) point-like objects can help us to develop better systems in traffic management, environment protection, resource management, surveillance, military industry, informatics, etc. These analyses can be supported by GIS systems. Data collection is the one of the most important part of these systems. This paper discusses those techniques which can provide location information in real-time or after post-processing. These movements can be observed by different sensors; however, these sensors cannot provide all information about the movements. In most cases we do not know the exact positions of the entity (the object/individual to be tracked) due to the inappropriate accuracy and frequency of the positioning data. The aim of the investigations is to detect and/or determine various features of the movements. In order to examine the main and interesting characteristics of the movements it is important to choose an appropriate data acquisition technology for our purposes and the tracking method has to be adequately chosen to the particular type of movement (e.g. high speed movements can be tracked by high-frequency measurements). With the tracked dataset we have a chance to create models that describe the movements and/or their important characteristics. The primary aim of the model is to get predictive tools. While investigating the behavior of movements the three components (movement, tracking and model) must be consistent with each other. For example see a falling ball example in Fig. 1. The ball follows real trajectory (Fig. 1a) that is tracked in discrete points (Fig. 1b). Analyzing these tracked results the model can be created (Fig. 1c). Note that the example is very simple but shows the clear differences. Fig. 1. Movement, tracking and model In this paper I analyze the tracking methods. In order to group the different types of data acquisition techniques, some properties will be introduced that describe the main features of the movements. With these metrics the appropriate tracking technology can be chosen for the selected purpose. Properties of movements Following properties were defined in the next points: Degree of freedom: The movements can have physical limits that determine the degree of freedom. For instance in general vehicles follow the lanes of the roads, or in case of a museum gate the people can only walk through it. Thus, we can distinguish n-degree(s) of freedom of movements. 0-degree means the point-like movements, e.g. when the entity interacts with the dedicated point or can be

2 Second Conference of Junior Researchers in Civil Engineering 7 described with it. For example entity goes through a loop-detector or in front of a video camera. The timestamps and other metrics of these events are recorded and can be used for further analysis. The 1-degree freedom type movement can be described by line, curve or polyline, i.e. in one dimension geometry. On a certain modeling level, this type of movement is typical for vehicles, boats, tourists, etc. Note that this kind of movements is led by strong constraints that determine the trajectory. In contrary, 2-degree freedom movements allow free moves on a full 2D space, e.g. human or animal mobility, spread of viruses, etc. Note that this metrics belongs to the movements, but it also depends on the tracking method. The dataset that the tracking method provide, can reduce the movements degree of freedom, thus this metric can be connected with the particular method. Consistent with aforementioned definition the 3 degree of freedom movements also can be defined. Coverage: The extent of the movement can be interesting for different purposes. The extent determines the limits of the movements that strongly influence the chosen data acquisition techniques that have limited availability. Different tracking method has different coverage; not all technique covers the whole earth. By coverage these techniques can be divided to indoor and outdoor solutions Number of objects: In some application only one or few objects have to be examined, but other investigation needs information of movements of thousands of objects. The last one is called mass data, and widely used in human, animal or road traffic researches. These data are analyzed with statistical way, and support that research purposes which want to understand the behavior of objects, entities or individuals. Time-dependency: In most cases the movements depend on time, but in some use cases the time factor is not necessary to be known. In GIS modeling Gudmundsson et al. distinguish trajectory from curve [1]. While the trajectory means time-dependent movements, curve represents time-independent movements. The time-independent movement analysis is easier due to conventional, static information GIS management. Movement data tracking method This section presents some technologies and use cases of movement data acquisition. In Fig. 2 the overview of these methods can be seen with the previously defined properties. Besides movement properties, the type of raw data is also an important column in the table; the locations can not only be given by coordinates in a selected reference system, but this information can be represented by different location identifiers such as geocodes, cells, or many other ways. In case of identifiers (geocodes or cells), key-position pairs have to be defined. In some use cases accurate positions are not necessary, but the relative distance or other geometrical or topological information can be used instead. As the figure shows, I also present some potential application field that can be supported by the acquired data. Besides, the mass data gathering ability of the tracking method is also analyzed due to it can be important for statistical analysis of the movements. The bases of the methods are location estimation techniques, but these methods are analyzed from movements points of view. GPS tracking GPS-based positioning techniques can be used for tracking vehicles, for surveillance and security purposes, and in monitoring systems. This technology can rapidly provide accurate coordinates with high frequency rate. GPS is widely used for tracking any movements [5][6]. The critical questions of GPS tracking are the energy consumption, downloading and recording positioning data due to the small size of devices and limited hardware resources (battery, storage, etc.). To decrease the energy consumption the sampling rate can be reduced as well. Sometimes the positioning calculations are not carried out on board but during the postprocessing phase, and the tracking device only records the raw GPS signals. To decrease the size of positioning log files, different compressing techniques can be used. In order to get the acquired positions, either the GPS units have to be collected or remote connection can be used to download them [4]. From the human mobility tracking point of view, the GPS is much easier available than a couple of years ago due to the high penetration of smart phones and mobile devices. Using different location-based services (LBSs) these devices can be connected to a server, thus the collection of GPS data is also solved. This solution enables to observe the user s movements in real time. The GPS can also be used for traffic

3 Second Conference of Junior Researchers in Civil Engineering 8 measurement; it can be applied in individual and even in transport management systems. Tracking trucks or other vehicles supports the continuous monitoring of a company s resources. Fig. 2. Tracking method properties and applications Even though GPS is a widely used, mature technology, it has some disadvantages. Although the GPS solutions can be used world-wide, its signal is not available in indoor or any other GPS-denied environment. The battery and data gathering limits were mentioned before. The GPS is neither a convenient method for mass data collection, if the aim is to collect data from thousands of people. Mobile network based tracking The advantage of the method is the availability in indoor and covered locations, but the accuracy is less than that of GPS. The range of the tracking method is same as the mobile network coverage, and the accuracy depends on the applied location estimation solution. These solutions are previously used in LBS applications, and the network-side solutions support the traffic and urban analysis [9]. Mobile network based data acquisition can be divided into two categories: network-side, and terminal-side methods. The most penetrated technology in mobile communication is the Global System for Mobile Communications (GSM), but the developed positioning techniques also can be used in higher (3 rd or 4 th ) generation mobile communication networks, such as UMTS [7]. In modern mobile networks it is necessary to handle and store location information about the connected devices. In real systems it means that the network stores the ID of the transmission tower while the connection is established in communication (in GSM terminology it is the Cell-ID). When the communication is not established, the network records a larger area ID (in GSM technology it is location area network, LAC). The IDs can be paired with coordinates; these set of IDs can be acquired from the database of the network and can be used for analyzing the devices movements. This concept supports using these networks for positioning purposes and this concept enables the network-side data gathering. The established connection provides more accurate location information, but this is only available when the user calls somebody, so the availability of this method depends on the phone-using habits. However the location information can be available when the device is only just turned on (not calling), but the locations

4 Second Conference of Junior Researchers in Civil Engineering 9 are less accurate, and that usually does not satisfy the examination requirements. Other network-side location estimation method is Uplink-Time Difference of Arrival (U-TDoA), which can also provide collect client positions, but special hardware elements have to be equipped on the network infrastructure, but its accuracy ( meters) is better than in case of Cell-ID concept. One special type of Cell-ID-based location estimation is based on the log-mechanism of the network, i.e. when the mobile device changes between cells (handover - HO). If these changes and their coordinates can be determined in the measurement phase, these values can be paired in real time phase [9]. This method called HO-based location estimation that can be effectively used in traffic flow estimation. The Cell-ID and HO-based location estimation is also can be used on terminal-side since the device also records these information. Other terminal-side measurement uses the special hardware elements of the network for location estimation purpose (e.g. these concept are Angle of Arrival, Enhanced-Observed Time Difference, etc.) [7]. These methods acquire position at accuracy between 50 and 250 meters; some of them can be solved on network-side. Currently most of the networks are not equipped with these hardware elements, thus the technology is still not available in all mobile communication networks. Another method in client-side tracking is based on received signal strength (RSS) sensing called fingerprinting. In GSM-based fingerprinting the signal strengths of towers are measured and recorded with its geometrical positions before usage, and in the tracking phase, these coordinates and RSS values of the towers can be paired. This technology is well used for indoor tracking in urban areas [10]. Wi-Fi based tracking Wireless networks can effectively support indoor 2-degree freedom tracking methods, thanks to Wireless LAN (WLAN) infrastructure has been already deployed in many buildings. The WLAN networks are based on the IEEE standard. The wireless transmitters are called access points (AP); all APs cover a particular region. In network-based tracking the APs observe the RSS and signal-to-noise ratio (SNR) using the Media Access Control (MAC) address of the mobile device. A central server gathers these observations from the APs that can calculate the terminal positions. This method also can be used for mass data acquisition via stored locations in database. In terminal side the simplest method is similar to that of Cell-ID. In this case the device identifies the nearest AP with the best received signal. The AP can be determined with its Basic Service Set Identifier (BSSID). If the location of the AP is known, the mobile device s location is also known. By observing different AP of RSS and SNR value, the terminal can also determine its position with trilateration [11]. In case of WLAN positioning, the fingerprinting technique also can be used. These solutions apply deterministic or stochastic methods in order to match RSS signals with values stored in the database. With this method less than 3 m accuracy can be achieved [12]. Other technologies Bluetooth is another network technology that can be used for positioning. Every Bluetooth device collects information on other Bluetooth-enabled devices that can be found in its range (5-10 meters). Applying this technology the limited area has to be taken into consideration. The concepts of Bluetooth positioning as cellular positioning is same as in the WLAN case. This solution can be used in both indoor and outdoor applications. If some device positions are known (server devices), the client devices can identify it and can localized its position. The ultra-wide band (UWB) technology was presented in 1960 s, but using it for positioning is a relatively new topic due to regularization. The UWB can be used for telecommunication and as radar as well (see explanation later). The technology uses the wide spectrum of frequency and very short pulse with low energy for transmission. Thanks to these parameters, the range measurement between UWB units is more accurate than in case of other methods (few centimeters). From the movement tracking s point of view the UWB can be either passive or active method. As active method the UWB unit can be attached to the moving target, and after range measurement to the other fixed units, the coordinate determination can be based on different methods (signal strength, time-of-arrival, time-difference of arrival, ant its variants). As passive method the UWB can be used as radar. The UWB unit transmits signals that are scattered from objects. The UWB makes a scan and determine the general received signal shape about the environment. As a moving target enters this

5 Second Conference of Junior Researchers in Civil Engineering 10 area, the received signal form is changed, and after the detection of change, the distance between unit and the target can be calculated. Using multiple units the target coordinates are determinable. The UWB is hopefully a potential technique for accurate positioning in indoor navigation. Inductive loop-detector is a good example for 0-degree freedom systems; it counts the number of vehicles that pass it. From the detector s perspective it can record datasets with high availability that is not depending on the movements. Video camera also can be used for counting vehicles or detecting human movements. As opposed to loop detector, video camera can distinguish objects from each other, thus the movements can be identified. The main application field of this equipment is in traffic management. Conclusion In this paper I presented some properties of the movements. The degree of movements covers the spatial moving possibility of the object, the coverage defines the extent of the movement and the time-dependent property means whether time information is the essential part of the movements or not. These properties support choosing appropriate tracking methods. Some of these techniques have been presented from the movement s point of view. The GPS is widely used for providing 2-degree of freedom data, but it cannot be used in GPS-denied environment (e.g. indoor or in urban canyons). Other solutions, especially mobile communication based positioning can provide mass amount of data. On indoor positioning the Bluetooth, Wi-Fi, and ultra-wide band solutions were discussed. The UWB can provide the most accurate positions of these techniques, but it is not widely used. In contrary, Wi-Fi and Bluetooth networks can be found in most of the buildings, and their deployment is easy. With different solutions they are applicable for tracking 2- degree of freedom movements. Acknowledgement The work reported in the paper has been developed in the framework of the project Talent care and cultivation in the scientific workshops of BME" project. This project is supported by the grant TÁMOP B-10/ References [1] Gudmundsson, J., Laube, P., Wolle, T.: Computational Movement Analysis, Springer Handbook of Geographic Information (2012), pp [2] Song, C., Qu, Z., Blumm, N., Barabási, A-L.: Limits of Predictability in Human Mobility, (2010), Science, pp , vol. 327 [3] Eagle, N., Pentland, A.: Reality Mining: Sensing Complex Social Systems, (2006), Pers Ubiquit Comput, 10: [4] Tomkiewicz, S. M, Fuller, M. R., Kie, J. G., Bates, K. K..: Global Positioning System and Associated Technologies in Animal Behaviour and Ecological Research, (2010), Philosophical Transactions of the Royal Society, vol. 365, pp , 2010, doi: /rstb [5] Shoval, N.: Tracking technologies and urban analysis, (2008), Cities, vol. 25, doi: /j.cities [6] Spek, S., Schaick, J., Bois, P., Haan, R.: Sensing Human Activity: GPS Tracking, (2009), Sensors, vol. 9, pp , doi: /s [7] Küpper, A.: Location-Based Services: Fundamentals and Operation, (2005), Wiley [8] Gonzalez, M C., Hidalgo, C. A., Barabási, A-L.: Understanding individual human mobility patterns, (2008), Nature, vol. 453, pp [9] Koppányi, Z., Lovas, T., Barsi, Á., Demeter, H., Beeharee, A., Berényi, A.: Tracking Vehicle in GSM Network To Support Intelligent Transportation Systems, (2012), International Archives of Photogrammetry and Remote Sensing XXXIX:(B2) pp [10] Ahriz, I., Oussar, Y., Denby, B., Dreyfus, G.: Full Band GSM Fingerprints for Indoor Localization Using a Machine Learning Problem, (2010), International Journal of Navigation and Observation, 7 [11] Gustafsson, F., Gunnarsson, F.: Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements, (2005), Signal Processing Magazine, pp 41-53, vol. 22. [12] Lin, T. N.: Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks (2005), Wireless Networks, Communications and Mobile Computing, 2005 International Conference, pp , vol.2