1. Instructor : Dr. K. R. Rao Presented by: Rajesh Radhakrishnan

2.  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.

3.  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.

4. Fig1: [4]: Block diagram of spatio temporal object detection

5. Fig.2, eg: frame-8, an input to explain moving object detection. Following code were generated in MATLAB.

6. Fig 3: Background modeling by frame differencing. Raw image obtained after frame differencing

7. Fig.4. Foreground detection using threshold model =10(Fig 4.1), threshold model=40 (Fig.4.2) Fig(1)Fig(2)

8. Fig 5: Data validation – This is a parametric model of skin detection

9.  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.

10.  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.

11. 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.

12.  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.

13. 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.

14.  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.

15.  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.

16.  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.

17.  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.

18.  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.

19.  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.

20. Thank You