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TARGET TRACKING & CONTOUR DETECTION

Idowu Samuel Mohammad Khairul Muthanna Abdulhussein. Lulea University of Technology Department of Computer science and Electrical Engineering. TARGET TRACKING & CONTOUR DETECTION. AGENDA. Target tracking and WSN Application of Target Tracking VigilNET : A WSN Target Tracking Project

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TARGET TRACKING & CONTOUR DETECTION

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  1. Idowu Samuel Mohammad Khairul MuthannaAbdulhussein Lulea University of TechnologyDepartment of Computer science and Electrical Engineering TARGET TRACKING & CONTOUR DETECTION

  2. AGENDA • Target tracking and WSN • Application of Target Tracking • VigilNET: A WSN Target Tracking Project • Detection and Tracking Framework • Classifications • Techniques of Target Tracking • Contour Detection • Application of Contour Detection • Questions ?

  3. What is Target Tracking ? • Finding spatial coordinate of persons or objects on the move and supplying a timely ordered sequence of respective location data to a model • Target tracking is always an aftermath of target detection.

  4. WSN and Target Tracking • Target tracking is an important application of WSN • WSN are composed of large number or sensor nodes • nodes are small in size and communicate wirelessly in short distances • sensor nodes can perform sensing, data processing and communicating.

  5. WSN and Target Tracking • Each sensor node has multiple modalities for sensing the environment such as • Acoustic • Seismic • Light • Temperature, etc • each sensor can sense only one modality at a time. • These modalities are used in detecting and tracking a moving target

  6. Application of WSN Target Tracking • Military (surveillance, targeting): • surveilling troops on the battle field • detect, analyze, and predict the movement of hostile vehicles • Target identification • command and control • Weapon management and weapon guidance

  7. Application of WSN Target Tracking • Public (traffic control): • vehicles on the freeway • air traffic control • Healthcare and rescue: (tracking elderly, drug administration) • Border Security. • Intrusion detection

  8. Application of WSN Target Tracking • Disaster And Emergency Response • Monitoring Wildlife Animals: • Better understanding of region/animal relationship • Biodiversity

  9. VigilNET : A Target tracking Project • VigilNET • VigilNet is a wireless sensor network for military surveillance. • general objective of VigilNet is to alert military command and control units of the occurrence of events of interest in hostile regions • The events of interest are the presence of people, people with weapons, and large and small vehicles. • information obtained is reported to a remote base station within an acceptable latency

  10. VigilNET

  11. VigilNET • VigilNet is an operational self-organizing sensor network • Implores over 200 XSM mote nodes • Helps in unmanned surveillance where high degree of stealthiness is required

  12. VigilNET

  13. VigilNET: really? • The VigilNet architecture is built on top of TinyOS • VigilNet currently consists about 40,000 lines of NesC and Java code, running on XSM, Mica2 and Mica2dot platforms. • Designed to scale to at least 1000 XSM motes and cover minimal 100x1000 square meters to ensure operational applicability. • Also, VigilNet project is sponsored by DARPA (Defense Advanced Research Projects Agency) • http://www.cs.virginia.edu/wsn/vigilnet/index.html

  14. Why sensor networks? Why not GPS? • Location tracking is done using GPS. However, GPS has its limitations. • Some of the limitations are: • It cannot be used in most indoor environments because It depends on Line of Sight. • Also in non-urban outdoor settings, GPS does not yield accurate results because it depends too much on factors such as terrain, foliage and topographical settings of the place where the object is located. • GPS receivers may be too large, too expensive or too power intensive

  15. Why sensor networks? Why not GPS? • Using wireless sensor networks provides us with a better option • since the nodes are relatively small, inexpensive and low power devices. • They are much more viable considering economic and convenience constraints.

  16. Detection and Tracking Framework • Tracking system require the sensors to work in groups in order to improve the reliability of target tracking algorithms. • Nodes needs to be coordinated in some way • The 3 target tracking techniques used are • Tree based • Cluster based • prediction based

  17. Tree Based • 2 types of tree based techniques • Scalable Tracking Using Networked sensors (STUN) • Dynamic Convoy Tree-based Collaboration (DCTC)

  18. Scalable Tracking Using Networked Sensors (STUN) • handles a large number of moving objects at once. • uses a hierarchy to connect the sensors • The leaves are sensors. • The querying point as the root. • The other nodes are communication nodes.

  19. Scalable Tracking Using Networked Sensors (STUN)

  20. Scalable Tracking Using Networked Sensors (STUN) • Advantage • Message pruning • Routing • Disadvantage • Building the tree structure

  21. Dynamic Convoy Tree-Based Collaboration (DCTC) • DCTC relies on a tree structure called “convoy tree” • The tree is dynamically configured to add some nodes and prune some nodes as the target moves.

  22. Dynamic Convoy Tree-Based Collaboration (DCTC)

  23. Cluster-Based • Static Clustering. • Dynamic Clustering.

  24. Target Classification • SINGLE TARGETS • such as a live body under rubble. • MULTIPLE TARGETS • such as various animals monitored in their habitat.

  25. Tracking of a Single Target • Suppose a target enters Cell A. Tracking of the target consists of the following five steps:

  26. Tracking of a Single Target • Some and perhaps all of the nodes in Cell A detect the target. • each time instant, the manager nodes determine the location of the target from active nodes • The manager nodes use locations of the target to predict the location of the target future time instants. • The predicted positions of the target are used to create new cells that the target is likely to enter • Once the target is detected in one of the new cells, it is designated as the new active cell

  27. Tracking of a multiple target (MTT) • In the simple case • Targets occupy distinct space-time cells • Multiple instances of algorithm can be used in parallel • A varying number of indistinguishable targets • Arise at random in space and time • Move with continuous motions • Persist for a random length of time and disappear • Goal: For each target, find its track!!!

  28. Tracking of a multiple target (MTT) • Existing Algorithms(MTT Algorithms) • MHT (Multiple Hypothesis Tracker) • JPDAF (Joint Probabilistic Data Association Filter) • MTMR, PMHT, etc.

  29. Classification of Target Tracking The two approaches for Target Tracking WSN are • Centralized target • Distributed

  30. Centralized Approach • Sensors in the sensing network detect the target and send the target signatures to the Base Station (BS) • BS determines whether there is a target or not by using the target signatures sent from the sensing nodes and tracks if there is the target • There may be many sensors transporting target information to BS at the same time • BS runs out of power faster because of information overload

  31. Distributed approach • The whole sensor network is divided into regions in form of clusters • There is one manager node in each region (cluster head) • The processing tasks are performed at the manager nodes, not only at base station.

  32. Target Localization • Localization primarily refers to the detection of spatial coordinates of a node or an object • Trilaterationtechnique: • Intersection of three circles is used to determine the object location • While object is being tracked by three sensors, distance to it from a fourth sensor is also being calculated simultaneously. • the distance information and a simple mathematical technique is used in predicting the target’s position • Forth sensor node is not used for detection but only for estimation of the target’s location.

  33. Contour Detection in WSN

  34. Contour • The Contour in geometry is an outline especially of a curve or irregular figure • It is the track of the boundaries of interest that captures some topological changes

  35. Contour Detection in WSN • Contour detection is carried out on a sensor field • The sensor field often spans over a large geographic area and encompasses hundreds of thousands sensors to observe a particular physical phenomenon. • A contour map is a useful data representation schema that provides an efficient way to visualize the field monitored by sensor networks.

  36. Contour Detection in WSN

  37. Contour Detection in WSN • A group nodes Generate contour maps for the region which it covers • Contour lines(isoline) offer more detailed information about the underlying phenomenon such as signal’s amplitude, density and source location. • Contour maps provide an efficient way to visualize fields sensed by wireless sensor networks.

  38. Application of Contour Detection • Mining Industry. • Used in Face recognition, Pattern matching object tracking in the pedestrian tracking network. • Transportation Congestion. • Environmental Monitoring. • Industrial sensing and diagnostics

  39. …Questions

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