The project’s goal is to develop a computationally efficient, robust real-time particle filter-based visual tracker. In particular, we aim to increase the robustness of the tracker when it is used in conjunction with weak (but computationally efficient) appearance model, such as color histograms. To achieve this goal, we have proposed an adaptive parameter estimation method that estimates the statistic parameters of the particle filter on-line, so that it is possible to increase or reduce the uncertainty in the filter depending on a measure of its performances (tracking quality).
The method has proved to be effective in dramatically increasing the robustness of a particle filter-based tracker in situations that are usually critical for visual tracking, such as in presence of occlusions and highly erratic motion.
The data set we used is now available for download, with ground truth data, in order to make it possible for other people to test their tracker on our data set and compare the performance.
It is made of 10 video sequences showing a remote controlled toy car (Ferrari F40) filmed from two different point of view: ground floor or ceiling. The sequences will be provided in mjpeg format, together with text files (one per sequence) containing ground truth data (position and size of the target’s bounding box) for each frame. Below you can see an example of the ground truth provided with our data set (sequence #10):
We have tested the performance of the resulting tracker on the sequences of our data set comparing the segmentation provided by the tracker with the ground truth data. Quantitative measures of this performance are reported in the literature. Below we show a few videos that demonstrate the tracker capabilities.
This is an example of tracking on sequence #9 of the data set:
An example tracking humans outdoor with a PTZ camera. In this video (not in the data set) the camera was steered by the tracker. It is thus an active tracking and it shows that the method can be applied to PTZ cameras, since it does not use any background modeling techinque: