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UT Arlington EE 5359 - EE 5359 LECTURE NOTES

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Statistical analysis and evaluation of spatio-temporal and compressed domains moving object detectionMotivationKey parameter:Spatio-temporal Object detectionBackground Modelling-Frame differencingSlide 6Slide 7Slide 8Compressed Domain Object detectionFour main processes:Slide 11Initial Region ExtractionCreating and updating unmoving regions during object stopSlide 14Work to be doneScope of the projectStatistical analysisReference:Slide 19Slide 20Instructor : Dr. K. R. RaoPresented by: Rajesh Radhakrishnan Not much work had been presented in evaluating the functionality of the compressed domain object detection with that of the spatio-temporal one. More research work is going on to improve the efficiency of compressed domain object detection to be used for computer vision application. The key parameter to perform object detection is to determine the optical flow in case of spatial-temporal detection, and motion vector estimate in case of compressed domain detection.Fig1: [4]: Block diagram of spatio temporal object detectionFig.2, eg: frame-8 , an input to explain moving object detection. Following code were generated in MATLAB.Fig 3: Background modeling by frame differencing. Raw image obtained after frame differencingFig.4. Foreground detection using threshold model =10(Fig 4.1), threshold model=40 (Fig.4.2) Fig(1) Fig(2)Fig 5: Data validation – This is a parametric model of skin detection Only motion vector information is required.  Recent work in compressed domain object detection is by vector featured image algorithm. [2]. This algorithm is efficient enough to detect pauses in moving object. Initial region extraction: Involves converting data from encoded format to display format.1. Form a initial region definition making use of current block Bc, reference block Br and background block Bb.[2].2. Moving region detection: involves labeling of blocks as moved Bm and unmoved region bu. These five blocks form the basis of object detection algorithm.3.Modification of vector-featured regions:This is the module where the pauses in moving object are detected.4. Final step involves moving object tracking. Fig 6 :Initial region extraction,[2]Three Blocks directly extracted from motion vectors, they are current block Bc, reference block Br and background block Bb.Fig 7:updating of moving and unmoving regions[2].Here, additional two blocks are included to detect object stops, they are moving block Bm and unmoving block Bu.A mapping is done between the current and the next frame and new regions are marked by Bc and overlapping regions as Bm and non-zero to zero motion vector are marked as Bu. To get moving object regions, extract minimum bounding rectangles MBR’s which mark the regions of moving object in a video. To implement a compressed domain object detector that can recognize moving hand location. To implement a compressed domain based moving object detector. Then generate a time series containing a centroid of detected object, with detection box size of 40x40. Three essential modules are required to obtain the comparative study of the moving object detection between two domains. First is the manual annotation of hand locations using a GUI to get the co-ordinate location of the hand in every frame. Second is to obtain time series of hand locations based on spatio-temporal algorithm. Third is to obtain time series of hand locations based on compressed domain algorithm. Hand locations of the detected hand are going to be compared with annotated hand locations to find efficiency. Efficiency to detect multiple hand locations and execution time of both the algorithms will be tested. More parameters may be added for future considerations. Z. Qiya and L. Zhicheng, “Moving object detection algorithm for H.264/AVC compressed video stream”, ISECS International Colloquium on Computing Communication, control and management, pp 186-189, Sep, 2009.  T. Yokoyama, T. Iwasaki, and T. Watanabe,” Motion vector based moving object detection and tracking in the MPEG Compressed Domain”, Seventh International Workshop on content based Multimedia Indexing, pp 201-206, Aug, 2009.  Kapotas K and A. N. Skodras,” Moving object detection in the H.264 compressed domain”, International Conference on Imaging systems and techniques, pp 325-328, Aug, 2010. Sen-Ching S. C and C. Kamath,” Robust techniques for background subtraction in urban traffic video” Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Jul, 2004. S. Y. Elhabian, K. M. El-Sayed,” Moving object detection in spatial domain using background removal techniques- state of the art”, Recent patents on computer science, Vol 1, pp 32-54, Apr, 2008. O. Sukmarg and K.R Rao,” Fast object detection and segmentation in MPEG compressed domain”, TENCON 2000, proceedings, pp 364-368, Mar, 2000. W.B. Thompson and Ting-Chuen P,” Detecting moving objects”, International journal of computer vision, pp 39-57, Jun, 1990.Thank


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UT Arlington EE 5359 - EE 5359 LECTURE NOTES

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