[4eyes] Reading Group - Friday 2/22/13

[Chris Sweeney]

Reading group in just over an hour! We'll meet at 11:15 today instead of
the usual 11

Chris
On Feb 19, 2013 5:09 PM, "Benjamin Nuernberger" <bnuernberger at cs.ucsb.edu>
wrote:

> Also, let's plan to meet at 11:15am. I have to help proctor an exam that
> ends around 11am on the other side of campus.
>
> On Tue, Feb 19, 2013 at 5:03 PM, Benjamin Nuernberger <
> bnuernberger at cs.ucsb.edu> wrote:
>
>> Hi everyone,
>>
>> Let's discuss the following paper this Friday at the reading group. It
>> describes a robust, real-time approach to tracking objects that improves
>> over time using machine learning. You can see some of the project's videos
>> here: http://info.ee.surrey.ac.uk/Personal/Z.Kalal/tld.html.
>>
>> Cheers,
>> Ben
>>
>> Tracking-Learning-Detection
>> IEEE Transactions on Pattern Analysis and Machine Intelligence, July 2012
>> Zdenek Kalal, Krystian Mikolajczyk, and Jiri Matas
>> http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6104061
>>
>> Abstract:
>> This paper investigates long-term tracking of unknown objects in a video
>> stream. The object is defined by its location and extent in a single frame.
>> In every frame that follows, the task is to determine the object's location
>> and extent or indicate that the object is not present. We propose a novel
>> tracking framework (TLD) that explicitly decomposes the long-term tracking
>> task into tracking, learning, and detection. The tracker follows the object
>> from frame to frame. The detector localizes all appearances that have been
>> observed so far and corrects the tracker if necessary. The learning
>> estimates the detector's errors and updates it to avoid these errors in the
>> future. We study how to identify the detector's errors and learn from them.
>> We develop a novel learning method (P-N learning) which estimates the
>> errors by a pair of “experts”: (1) P-expert estimates missed detections,
>> and (2) N-expert estimates false alarms. The learning process is modeled as
>> a discrete dynamical system and the conditions under which the learning
>> guarantees improvement are found. We describe our real-time implementation
>> of the TLD framework and the P-N learning. We carry out an extensive
>> quantitative evaluation which shows a significant improvement over
>> state-of-the-art approaches.
>>
>>
>
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