MSc Dissertation Proposal 2016/2017
Neuromorphic cameras report intensity changes for each pixel independently and
asynchronously. These cameras have a time resolution in the order of the μs and have a high dynamic range which makes them
interesting for tracking and visual navigation. Nonetheless, the output of
these cameras cannot be used directly with computer vision algorithms.
In this work, we intend to focus on target tracking
using a stream of events.
The objectives of this work are:
1.
- Neuromorphic
camera model definition.
2.
- Develop an intensity image
reconstruction algorithm.
3. - Develop an algorithm for target tracking by optimizing camera
resources.
N.A.
ISR / IST
Neuromorphic
cameras are biologically inspired cameras. The inspiration for their design
comes from the transient pathway of primate vision, which processes information
due to luminance changes in the scene.
These cameras do not report the intensity values for every pixel on
the sensor but report for each pixel, independently and asynchronously,
intensity changes above a given threshold. This allows reducing the redundant
information for pixels with unchanged intensity values. Furthermore, these
cameras have a time resolution in the order of the μs
and have a high dynamic range which makes them interesting for tracking and
visual navigation.
Nonetheless, these events cannot be used directly with computer
vision algorithms that are designed to operate on a frame basis. In the recent
years, there has been an effort to adapt computer vision algorithms to neuromorphic vision, namely, optical flow, corner and edge detection, epipolar
geometry.
In this work, we want to bridge the gap between computer and neuromorphic vision by focusing on target tracking. Target
tracking using neuromorphic sensors is a relatively
simple task on an environment with a moving object and a static background.
Nonetheless, this task becomes more complex when we consider a moving
background. In this situation, we cannot differentiate the events generated by
the object being tracked from the ones generated by the background. This task
becomes even more complex if we have more targets than observers.
The workplan consists on the study of the neuromorphic camera, and implementation of a tracking algorithm based on a stream of events. The results will be compared with standard computer vision algorithms for target tracking. Since computer vision methods cannot be applied directly to neuromorphic datasets, an intensity image reconstruction algorithm should also be implemented to obtain a frame based video from a stream of events. These methods will be applied to real datasets.
More MSc dissertation proposals on Computer and Robot Vision in:
http://omni.isr.ist.utl.pt/~jag