MSc dissertation proposal 2015/2016

 

Object Recognition in Omnidirectional Cameras

 

 

Introduction:

 

Omnidirectional cameras are ubiquitous in robotic and surveillance applications due to their omniawareness capability. Nonetheless, strategies that use directly the highly distorted images are scarce.

 

Many of the alternatives require unwarping the omnidirectional images to perspective images and then applying conventional algorithms. However, image unwarping requires a large amount of computation and introduces noise in image [Daniilidis2002]. Therefore, it is necessary to adapt the current algorithms to work directly with omnidirectional images. This can be accomplished by introducing the variable geometry into these algorithms.

 

In this work we intend to develop an object detection algorithm that takes into account the geometry of omnidirectional cameras and that is also resilient to deformations and occlusion. A comparison with existing algorithms for omnidirectional images should also be considered.

 

 

Objectives:

 

Objectives

The objectives of this work are: (i) acquire a catalogued dataset of omnidirectional images, and (ii) detect and recognize objects in omnidirectional images.

 

 

Detailed description:

 

The process of detecting objects by matching a pair of images consists of three main steps:

1.         Identify points of interest in both images. The localization of the points can be found with algorithms like the Harris corner detector [Harris1988]. Add some image data to those points in order to make them discernible and comparable with other points.

2.         The points of interest are mutually compared and the most similar ones are paired into correspondences. Here, we can have points that are matched incorrectly (outliers) and matched correctly (inliers).

3.         Final processing in order to minimize the number of outliers in the correspondences.

 

Normally, for the last two steps there are used rectangular windows for cameras with perspective projection but these are not appropriate for omnidirectional cameras [Svoboda2001]. Alternatives require image unwarping which remove distortion from the omnidirectional image but requires a large amount of computation and introduces noise in image [Daniilidis2002].

 

Svoboda et al. [Svoboda2001] developed a way of computing an adequate boundary for image matching in omnidirectional images. They defined the neighborhood on the surface of a mirror and then projected a small patch on the omnidirectional image plane. Similar strategies have been applied for Ieng et al. [Ieng2003] that propose a patch of different angular aperture for the varying resolution of the omnidirectional camera. Other approaches [Demonceaux2006] are derived from the equivalent sphere theorem proposed by Geyer and Daniilidis [Geyer2001].

 

Tang et al. [Tang2015] used this strategy to develop a pedestrian tracking algorithm in omnidirectional images that also uses a part-based methodology to accommodate for deformation and occlusion of pedestrians.

 

 

Main Steps

This work will focus on adapting algorithms for detecting objects in traditional cameras to omnidirectional cameras. In this work we intend to:

- Acquire a catalogued dataset of omnidirectional images.

- Develop an algorithm to detect and recognize objects in omnidirectional images.

- Compare results with existing algorithms that use omnidirectional images (directly or unwarped).

 

 

References:

 

[Harris1988] Harris, Chris, and Mike Stephens. "A combined corner and edge detector." Alvey vision conference. Vol. 15. 1988.

 

[Svoboda2001] Svoboda, Tomáš, and TomᚠPajdla. "Matching in catadioptric images with appropriate windows, and outliers removal." Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2001.

 

[Daniilidis2002] Daniilidis, Kostas, Ameesh Makadia, and Thomas Bulow. "Image processing in catadioptric planes: Spatiotemporal derivatives and optical flow computation." Omnidirectional Vision, 2002. Proceedings. Third Workshop on. IEEE, 2002.

 

[Ieng2003] Ieng, Sio-hoď, Ryad Benosman, and Jean Devars. "An efficient dynamic multi-angular feature points matcher for catadioptric views." Computer Vision and Pattern Recognition Workshop, 2003. CVPRW'03. Conference on. Vol. 7. IEEE, 2003.

 

[Demonceaux2006] Demonceaux, Cédric, and Pascal Vasseur. "Markov random fields for catadioptric image processing." Pattern Recognition Letters 27.16 (2006): 1957-1967.

 

[Geyer2001] Geyer, Christopher, and Kostas Daniilidis. "Catadioptric projective geometry." International Journal of Computer Vision 45.3 (2001): 223-243.

 

[Tang2015] Tang, Yazhe, et al. "Parameterized Distortion-Invariant Feature for Robust Tracking in Omnidirectional Vision."

 

 

Requirements (grades, required courses, etc):

-

 

Expected results:

 

At the end of the work, the students will have enriched their experience in computer vision. In particular are expected to develop and assess the following topics:

- Dataset acquisition and cataloging.

- Omnidirectional camera calibration.

- Object detection and recognition from omnidirectional images.

 

 

Place for conducting the work-proposal:

ISR / IST

 

More MSc dissertation proposals on Computer and Robot Vision in:

 

http://omni.isr.ist.utl.pt/~jag