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        <title>m1w1</title>
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        <description>Module #1: A Basic Surveillance System

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Week #1: Setup, Data, and Basic

Explore the datasets available and select a couple of videos to use as the starting point for processing, testing, and debugging the surveillance system to be created as part of this learning module.</description>
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        <description>Module #1: A Basic Surveillance System

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Week #2: Foreground Object Extraction

With the background modelling step done, we have a means to identify regions of the image that do not conform to the expected scene.  We will presume that these are all objects of interest to track, e.g.</description>
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        <description>Module #1: A Basic Surveillance System

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Week #3: Optimization-Based Data Association

Performing detection does provide a means to identify objects of interest versus the prevailing background image. However if we are interested in maintaining the identity of the objects, additional processing and logic is required.  The simplest scheme simply considers the spatio-temporal history of the targets and tries to link the current detected objects to the previously detected objects. This f…</description>
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        <description>Module #1: A Basic Surveillance System

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Week #4: Adding Temporal Dynamics via a Kalman Filter

We can do a better job handling things like occlusions, as well as improve the data association, by adding in a temporal filter.  A simple, powerful method is to recursively estimate and correct the moving target dynamics through a Kalman filter.  Matlab has a page on how to do so for</description>
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        <description>Module #2: Target Modelling and Re-Identification

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Here, we are going to step away from the video processing part for a moment and focus solely on the datasets that you have all generated.  We want to test out an algorithm for correctly matching the pairs of people based on their image information alone.  The more people in the dataset, the trickier it becomes. In a binary selection process, random guessing will get you a 50% correctness outcome. When selecting amongst 10 things, the…</description>
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        <description>Module #2: Target Modelling and Re-Identification

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Week #2: Testing the Method

Once we know the mixture models are proper, then the next step is to actually use the models as a means to test proximity of one model to another.  If two models are close, or the data from a model is consistent with the model, then two should be the same.  In this manner, we can identify when the same person comes in and out, or when it gets occluded and re-appears. This action is called $L_2$$$E(T ; M) …</description>
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        <description>Module #2: Target Modelling and Re-Identification

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Week #4: Appearance-Based Data Association

When two tracked people overlap, then there is some ambiguity as to which model the box really belongs to.  Usually at the initial phase of overlap, the two targets are still visible. Then after a bit, if one person really passes behind another, only the single person is visible. Eventually then, the person re-emerges but is still part of the same detection window. The final phase involves …</description>
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        <description>Module #2: Target Modelling and Re-Identification

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Week #3: Re-Identification

Now that you have a potentially viable re-identification module, the next step is to incorporate it into your system. That involves adding in the following functionality:</description>
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        <description>Surveillance Videos

This page lists several sources of surveillance videos.  The first set is associated with past or ongoing research efforts on the topic of video based surveillance, and usually involve specific content associated to some challenging aspect of video interpretation.  When getting started, it is usually best to identify the simplest videos.  These are usually videos with diffuse, consistent lighting (so, outdoors on an overcast day, or indoors but without glare on the floor), a…</description>
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