Detecting Abandoned Objects With a Moving Camera - PowerPoint PPT Presentation

Loading...

PPT – Detecting Abandoned Objects With a Moving Camera PowerPoint presentation | free to download - id: 7f1fb2-YTVhM



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Detecting Abandoned Objects With a Moving Camera

Description:

Detecting Abandoned Objects With a Moving Camera 9977003 Introduction Dynamic suspicious behaviors ... – PowerPoint PPT presentation

Number of Views:53
Avg rating:3.0/5.0
Slides: 18
Provided by: 5417
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Detecting Abandoned Objects With a Moving Camera


1
Detecting Abandoned Objects With a Moving Camera
  • ??????? ??
  • ?????? 9977003 ???

2
Outline
  • Introduction......................................
    ..3
  • Related work......................................
    4
  • Proposed approach.............................6
  • Intersequence Geometric Alignment........8
  • Intrasequence Geometric Alignment.......10
  • Temporal Filtering............................13
  • Experimental results.........................14
  • Conclusion........................................
    17

3
Introduction
  • Dynamic suspicious behaviors
  • Static dangerous objects

4
Related work
  • Spengler and Schiele
  • a tracking/surveillance system to automatically
    detect abandoned objects and draw the operators
    attention to such events
  • Porikli et al.
  • use two foreground and two background models for
    abandoned object detection
  • Guler and Farrow
  • use a background-subtraction based tracker and
    mark the projection of the center of mass of each
    object on the ground
  • Smith et al.
  • use a two-tiered approach

5
Related work
  • All of the previously mentioned techniques
    utilize the static cameras installed in some
    public places, where the background is
    stationary.
  • However, for some application scenarios, the
    space to keep a watch on is too large to use
    static cameras.
  • Therefore, it is necessary to use a moving camera
    to scan these places.

6
Proposed approach
  • Intersequence Geometric Alignment
  • Intrasequence Geometric Alignment
  • Temporal Filtering

7
(No Transcript)
8
Intersequence Geometric Alignment
  • SIFT feature descriptor (128-dimensional)
  • RANSAC v.s mRANSAC

9
  • Fig. 3. Examples of alignment based upon RANSAC
    and modified RANSAC (mRANSAC).
  • First column the reference and target frames.
  • Second column best inliers obtained from RANSAC
    and mRANSAC respectively.
  • Third column the aligned reference frames based
    upon RANSAC and mRANSAC.
  • Fourth column difference images between the
    aligned reference frames.

10
Intrasequence Geometric Alignment
  • Removal of False Alarms on High Objects
  • Removal of False Alarms on the Dominant Plane

11
  • Fig. 6. Illustration of intrasequence alignment
    for R and removing false alarms on high objects
    based upon the alignment results. See text in
    Section III-B-I for details

12
  • Fig. 8. Illustration of intrasequence alignment
    for both T and R, and removing false alarms in
    flat areas by local appearance comparison of the
    alignment results.

13
Temporal Filtering
  • Goal
  • To confirm the existence of suspicious objects.

14
Experimental results
  • The length of the 15 test videos ranges from 70
    to 230 frames.
  • The height of our test objects ranges from about
    480 cm.
  • There are about 23 suspicious objects overall and
    we successfully detect 21 of them.
  • Detection fails in only one video where the test
    object is almost flat.

15
(No Transcript)
16
Conclusion
  • Our algorithm finds these objects in the target
    video by matching it with a reference video that
    does not contain them.

17
Thank you everybody
About PowerShow.com