Object Detection

Research

Object Detection

Object detection consists in automatically localizing and recognizing in images the presence of objects belonging to a predefined set of categories, and it i...

Posts

Dictionary Learning

2 minute read

Recent successes in the use of sparse coding for many computer vision applications have triggered the attention towards the problem of how an over-complete d...

Exemplar-based Object Layout

2 minute read

Recognizing the presence of object classes in an image, or image classification, has become an increasingly important topic of interest. Equally important, h...

Transfer Learning from Multiple Sources

2 minute read

Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the availab...

Aerial Video Analysis

8 minute read

In aerial video moving objects of interest are typically very small, and being able to detect them is key to enable tracking. There are detection methods tha...

People Detection

3 minute read

People detection and tracking in video are fundamental Computer Vision capabilities that still constitute a research challenge. Important difficulties are du...

Publications

Moving object segmentation using scene understanding Perera, A. G. A., Brooksby, G., Hoogs, A., Doretto, G., and Kaufhold, J. Technical Report 2005GRC182, GE Global Research, 2005. Visualization and Computer Vision Laboratory abstract bibTeX
We present a novel approach to improving moving object detection algorithms for aerial video by incorporating scene understanding. We argue that scene understanding is a valuable input feature for moving object segmentation, and show how it can be used to reduce the false positive rate of any moving object detection algorithm. The approach is especially effective on sequences with large scene depth and much parallax, as often occurs with low, fast-moving sensors. In one difficult sequence, our results show an 8-fold reduction of false positives on average, with essentially no impact on the true positive rate, even when scene understanding is only moderately accurate. We show how scene understanding can be used to increase the accuracy of frame-to-frame homography estimates, and also present a novel approach that uses motion information from the previous frame for outlier rejection at the current frame to further increase accuracy.
```
@techreport{pereraBHDK05tr,
  author = {Perera, A. G. A. and Brooksby, G. and Hoogs, A. and Doretto, G. and Kaufhold, J.},
  title = {Moving object segmentation using scene understanding},
  institution = {GE Global Research},
  year = {2005},
  number = {2005GRC182},
  address = {Niskayuna, NY, USA},
  month = mar,
  note = {Visualization and Computer Vision Laboratory},
  bib2html_pubtype = {Tech Reports},
  bib2html_rescat = {Video Analysis, Visual Motion Segmentation},
  file = {pereraBHDK05tr.pdf:doretto\\report\\pereraBHDK05tr.pdf:PDF;pereraBHDK05tr.pdf:doretto\\report\\pereraBHDK05tr.pdf:PDF},
  keywords = {motion segmentation, moving object detection, scene understanding,
  	scene content, recognition},
  owner = {doretto},
  timestamp = {2006.11.29}
}
```