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Real Time Object Tracking

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Some discussion about real time object tracking and detection methods.

Some discussion about real time object tracking and detection methods.

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  • http://plaza.ufl.edu/lvtaoran/object%20tracking.htm
  • Image/appearance based trackingThere are wide application in real time object detection and tracking.
  • Multiobject (people) tracking within a video of a pedestrian passageway. The dynamic motion vectors attached to each individual represents direction of movement and speed.
  • Here, we can see how a mobile robot can detect and track this red ball. It moves accordingly to the red ball movement.
  • As the objects move over time, ther are different illumination and motions of small objects; due to perspective, occlusion, interaction between objects and appearance or disappearance of objects.2. to track targets and keep on detectingnew ones on a moving camera platform at the same time,the traditional motion detector based on the backgroundsubtraction can not be applied here.3. How to...In the same time..4. Because of comprehensive search ,it is hard to meet strict time constraint in real-time
  • To solve these problems...The choice of hardware can increase the performance of object detection and traking in real time which has hard time constraintsMemory is....There are several processor, such as FPGA depends on the choice of soft processorASICulfill the speed criteria of real-time, but it is complicatedGPU  increase the speed up the computation at the bottom level method (optical flow)
  • http://www.princeton.edu/~xm/vision05/Assignment3/Tracking/tracking.htm
  • The first term is proportional to the density estimate at x computed with the kernel G. the second term is the mean shift. This part is the mean of the window. We calculate it by using a kernel function, which gives different weights to all points inside the window. the mean shift vector thus always points toward the direction of maximum increase in the density.
  • http://www.codeproject.com/KB/GDI-plus/MeanshiftTracking.aspxChoose a search windowsize.2. Choose the initial locationof the search window.3. Compute the mean location(centroid of the data) in thesearch window.4. Center the search windowat the mean locationcomputed in Step 3.5. Repeat Steps 3 and 4 untilconvergence
  • Camshift is based on mean shift, but the window size is changed in in video sequence, when the object moves, the size and locations of color distributions will change over time. Mean shift. mean shift algorithm uses fixed window size. So it might fail. However, camshift can deal with this problem by adjusting the window size according to the distribution. Real time
  • 3. Firstly, initialize the Gaussian mixture model to get the background image, and then using the background differential with the current frame to detect the moving objects. Kalman filter is used to predict the centre of the searching window in the next frame. Then, camshift will find the optimum position of the target in order to modify the prediction. This can improve the speed of camshift algorithm and solve the occlusion problem.
  • Theses results are from the paper by Shah, in which he has demonstrated the results of the all the above mentioned methods for the same task of tracking a moving ball with DSP hardware.As we can see that the Lukas kanade is the fastest compared with the traditional methods.
  • The absolute difference and census transform are easy to implement but computationally expensive and slow. Feature based method can track multiple objects, but it is also slow.
  • KLT algorithm is can detect objects fast and accurately and it is robust to noise and dynamic scene. but it requires large memory, when the search window size is large.Mean shift has low computation cost. But it might fail in case of heavy occlusion and it can only detect single object. This can be solved by combining different algorithm, for example, SIFT feature descriptor and Kalman filter.
  • Transcript

    • 1. REAL-TIME OBJECT DETECTIONAND TRACKING By: Vanya V. Valindria Hammad Naeem Rui Hua
    • 2. Outline • Introduction • Hardware in RT Object Detetion & Tracking • Methods Traditional Methods: Modern Methods: Absolute Differences Census Method KLT Feature Based Method Meanshift • Result and Conclusion
    • 3. IntroductionDefinition:Object detection detect a particular object in an imageObject tracking to track an object(or multiple objects)over a sequence ofimages
    • 4. Application: Traffic Informationhttp://www.youtube.com/watch?v=vA35sXbn7zs
    • 5. Application: Surveillancehttp://www.youtube.com/watch?v=o25fClk9cdg
    • 6. Application: Mobile Robothttp://www.youtube.com/watch?v=Q4zycRGJFFs
    • 7. Problems??• Temporal variation/dynamic environment• Abrupt object or camera motion• Multi-camera? Multi-objects?• Computational expensive
    • 8. Hardware in Real-time Tracking• MEMORYImportant  Tracking system encountering limited memory problems.• FRAME RATE ~30 FPS• PROCESSORS - DSP• Allow saturated arithmetic operation• Powerful operation ability• Can do several memory accesses in a single instruction
    • 9. Object Detection and Tracking• In a video sequence an object is said to be in motion, if it is changing its location with respect to its background• The motion tracking is actually the process of keeping tracks of that moving object in video sequence i.e. position of moving object at certain time etc.
    • 10. Flow Chart Idle Image acquisition Object Detection Image acquisition Object tracking Object No Lost? Y es
    • 11. Method 1: Absolute Differences= Image subtraction D(t)=I(ti) – I(tj)Gives an image frame with changed and unchanged regionsIdeal Case for no motion: I(ti) = I(tj), D(t)=0
    • 12. Movingobjectsaredetected
    • 13. Results: Frame1 Frame10 Difference of Two Frames
    • 14. Absolute DifferenceMethods for Motion Detection  Frame Differencing  Background SubtractionDraw Backs:involves a lot of computations Not feasible for DSP implementation
    • 15. Method 2: Census Transforms124 74 32 1 1 0124 64 18 If (Center pixel < Neighbor 1 x 0 pixel) Neighbor pixel = 1157 116 84 1 1 1 Signature Vector Generation 1 1 0 1 x 0 Signature Vector 11011101 1 1 1
    • 16. Image128 26 125 243 87 Signature Vectors96 76 43 236 125 10110101 00101011128 129 235 229 209 Signatur vector generation for . all pixels228 251 229 221 234 . .227 221 35 58 98 10111010 List 10110101 Generation List 00101011 population . . . Generated List 10111010 Signature vector matching
    • 17. Census Transform: Advantages:  Compare only two values 0 or 1.  Similar Illumination Variation for pixel and neighbouring pixels Draw Backs:  As we only deal with only 0`s and 1`s, this method is sensitive to noise.  Calculate, store and match process  computationally Expensive
    • 18. Method 3: Morphology Based Object TrackingBackground Frame Object estimation differencing Registration
    • 19. Morphology Based Object Tracking • Image DifferencingBackground • ThresholdingEstimation • Contours are registered Object • Width, height and histogram are recorded for each contourRegistration • Each object represented by a feature vector (the length, Feature width, area and histogram of the object) Vector
    • 20. Segmented object Tracked object
    • 21. Morphology Based TechniquesAdvantages:Can Track Multiple objects  Objects are registered based on their anatomy Helpful for Object MergingDraw Backs:Object registration  complex and slow process For multiple object registration per frame  more complex
    • 22. Method 4: Lucas-Kanade Technique• Visual motion pattern of objects and surface in a scene  by Optical Flow Frame 1 Frame 2
    • 23. Method 5: Mean shift• An algorithm that iteratively shifts a data point to the average of data points in its neighborhood Choose a search window Compute the MEAN size in the initial location location in the search window Repeat until Center the search convergence window at the mean
    • 24. Intuitive Description Region of interest Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical balls
    • 25. Intuitive Description Region of interest Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical balls
    • 26. Intuitive Description Region of interest Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical balls
    • 27. Intuitive Description Region of interest Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical balls
    • 28. Intuitive Description Region of interest Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical balls
    • 29. Intuitive Description Region of interest Center of mass Mean Shift vector Objective : Find the densest region Distribution of identical balls
    • 30. Intuitive Description Region of interest Center of mass Objective : Find the densest region Distribution of identical balls
    • 31. Process
    • 32. CAMSHIFT--Continously Adaptive MeanshiftModified to adapt dynamically to the colour probability distributionsMore real time For each frame-> MEAN-SHIFT is applied with several iteration Store the location of the mean and calculate new window size for next frame
    • 33. New development• Combine with different features. SIFT features, colour feature & texture information• Camshift algorithm combined with the Kalman filter.
    • 34. Result Arithmetic and Time taken Algorithm Logic by operations Algorithm Absolute 4230100 16 Differencing Census 2416000 5. 4 Transform Morphological 352210 14.2 Tracking Kanade Lucas 500825 0.486
    • 35. Comparison Computationally Easy to implement expensive Absolute Differences Allows continuous Slow and low tracking accuracy Computationally expensive Census Transform Immune to noise and Illumination changes Complex if  Multiple objects per frame Can track multiple Slow Feature Based objects well Large Memory consumption
    • 36. Comparison High accuracy KLT Less execution time Large memory Robust to noise and dynamic scene Ineffective if Computationally less there is heavy MeanShift & CAMShift expensive occlusion
    • 37. Conclusion• KLT algorithm has the best performance with higher accuracy and less computation time• It requires combination of methods to achieve the appropriate object detection and tracking according to the proposed scenario
    • 38. References • S. Shah, T. Khattak, M. Farooq, Y. Khawaja, A. Bais, A. Anees, and M. Khan, “Real Time Object Tracking in a Video Sequence Using a Fixed Point DSP,” Advances in Visual Computing, pp. 879– 888. • K. Huang, L. Wang, T. Tan, and S. Maybank, “A real-time object detecting and tracking system for outdoor night surveillance,” Pattern Recognition, vol. 41, no. 1, pp. 432–444, 2008. • J. Li, F. Li, and M. Zhang, “A Real-time Detecting and Tracking Method for Moving Objects Based on Color Video,” in 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization. IEEE, 2009, pp. 317–322. • W. Junqiu and Y. Yagi, “Integrating color and shapetexture features for adaptive real-time object tracking,” IEEE Trans on Image Processing, vol. 17, no. 2, pp. 235–240, 2008. • Q. Wang and Z. Gao, “Study on a Real-Time Image Object Tracking System,” in Computer Science and Computational Technology, 2008. ISCSCT’08. International Symposium on, vol. 2, 2008. • Y. Meng, “Agent-based reconfigurable architecture for real-time object tracking,” Journal of Real-Time Image Processing, vol. 4, no. 4, pp. 339–351, 2009. • [Y. Yao, C. Chen, A. Koschan, and M. Abidi, “Adaptive online camera coordination for multi- camera multi-target surveillance,” Computer Vision and Image Understanding, 2010.