olgkad / graphtrack Goto Github PK
View Code? Open in Web Editor NEWA feature tracking algorithm implemented in C++.
A feature tracking algorithm implemented in C++.
How it's done now: A point of interest is marked in 5 different frames. Then candidate nodes are selected. In select_candidates_(), which is slow, approximate_distance_to_positive_patches() is computed for 5 positive patches. Then the path is traced.
How it's done in GraphTrack: A point of interest is only marked in the 1st frame. Then candidate nodes are selected. select_candidates_() will run faster now (5 times faster?) as it only checks one positive patch in each approximate_distance_to_positive_patches() call. Then the path is traced. If in a certain frame the point was marked incorrectly, we manually mark it (that changes the graph structure), and then we run Djikstra() again (which is inexpensive). No need to call select_candidates() again.
When the patch-size is increased to 20x20, the algorithm performs very poorly. However, small patches often do not capture the entire point of interest. When the part captured is too small, only local (and not global) features are captured, sometimes resulting in errors. (e.g. when run with "rabbit_fast.avi". Rabbits limb is too big to be accurately represented by a 10x10 patch.
The default values of variables below work well for tracking faces. Different values might need to be assigned for a particular video.
lambda_f
lambda_b
lambda_s
lambda_d
Need to add a constant to each node in a graph since as of now path lengths could be negative, and Djikstra may not always be returning the shortest path.
Think about building a web-application out of this
The system is already reasonably good at tracking a single point of interest. Now it needs to be extended to track multiple points of interest. I propose the following design:
Maybe they should be leaving a trace (trajectory) behind them.
Maybe just build the system good enough so that no two points are mislocated at the same time. That's not a robust solution though.
They could have different colours.
Besides, when marking the mislocated point, the system should know what rectangle (circle) we're trying to move to a different position. The best solution seems to be to make all the rectangles draggable. Implementing this, and assigning different colours to each mark should be enough.
In frames where interest points were marked, only one node should be included in the corresponding graph layer.
Currently, only 250 candidate nodes are being selected in each frame, and the rest is thrown away.
What actually should be done is 250 candidate nodes should be placed up in the priority queue, and only nodes at a distance >= 72 from positive patches should be discarded.
The candidates are chosen to be the N_nodes patches which are closest to the positive examples and are locally minimal. That last part is not accounted for right now.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.