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| ece4580:module_surveillance [2017/02/11 13:04] – pvela | ece4580:module_surveillance [2024/08/20 21:38] (current) – external edit 127.0.0.1 |
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| | ~~NOTOC~~ |
| ====== Surveillance System with a Static Camera ====== | ====== Surveillance System with a Static Camera ====== |
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| - Target recognition | - Target recognition |
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| | ===== Learning Modules ===== |
| | ---------------------------- |
| The sequence below introduces one aspect of surveillance systems at a time. They direct you to Matlab code that sometimes implements multiple steps at a time. It is recommended that you implement each one individually to get a sense for what role it plays in the entire system, rather than just copy/paste the whole system. | The sequence below introduces one aspect of surveillance systems at a time. They direct you to Matlab code that sometimes implements multiple steps at a time. It is recommended that you implement each one individually to get a sense for what role it plays in the entire system, rather than just copy/paste the whole system. |
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| ===== Module #1: A Basic Surveillance System ===== | |
| --------------------------------------------------- | |
| __** Week #1: Setup, Data, and Basics**__ \\ | |
| Explore the [[ECE4580:Module_Surveillance:MatlabVideos|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. | |
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| Meanwhile, check out this review of three [[http://www.eetimes.com/document.asp?doc_id=1275604|simple background modeling methods]]. You are not advised to use the code provided when implementing the same in future activities. There are more efficient ways to do the same without resorting to as many for loops, plus the implementation needs to be packaged up for use by the overall system. Plus, the activities below utilize existing Matlab libraries to the extent possible or desirable. | Module Set #1: A Basic (Foreground Detection-Based) Surveillance System |
| | - [[ECE4580:Module_Surveillance:M1W1|Week #1]]: Setup, Data, and Basics |
| | - [[ECE4580:Module_Surveillance:M1W2|Week #2]]: Foreground Object Extraction |
| | - [[ECE4580:Module_Surveillance:M1W3|Week #3]]: Optimization-Based Data Association |
| | - [[ECE4580:Module_Surveillance:M1W4|Week #4]]: Adding Temporal Dynamics via a Kalman Filter |
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| As a first step, obtain the [[http://pvela.gatech.edu/classes/files/ECE4580/Module_Surveillance/survSystem.m|surveillance system class stub]] and also the [[http://pvela.gatech.edu/classes/files/ECE4580/Module_Surveillance/mainLoop.m|main execution script]]. Modify the main loop code stub so it loads a video you've chosen, loops through the frames, displays the image associated the each frame, and quits when that's done. Naturally this code won't do any surveillance, but it will setup the system to do so. | Module Set #2: Target Modelling and Re-Identification |
| | - [[ECE4580:Module_Surveillance:M2W1|Week #1]]: Differentiating People |
| | - [[ECE4580:Module_Surveillance:M2W2|Week #2]]: Testing the Person Model |
| | - [[ECE4580:Module_Surveillance:M2W4|Week #3]]: Re-Identification in Action |
| | - [[ECE4580:Module_Surveillance:M2W3|Week #4]]: Enhancing Tracking |
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| As a second step, implement the basic background modeling detection step. Matlab has implementation of the [[https://www.mathworks.com/help/vision/ref/vision.foregrounddetector-class.html|mixtures of Gaussians adaptive background estimation algorithm]]. Just perform the estimation part and retrieve the binary foreground image. Modify the ''displayState'' function to display this output. When run, the system should display the source video, plus a binary image sequence associated to the detected objects. | Module #3: Merging and Splitting |
| | - TBD |
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| //Explore & Deliverables:// How well is the background modeled? You can identify how well it works by examining the quality of the binary image sequence. Does it capture the target objects only? Are there more false positives or false negatives than you like? What did you do to get the best result possible (to what parameters)? You should turn in at least one image pair showing the input frame, plus the output frame after foreground detection (or with the mask as noted in the code stub). | Module #4: Tracking vs Detection |
| | - TBD. |
| __** 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. //targets//. Advance beyond the current foreground modelling step to include processing of the binary foreground image for extraction of detected targets and their bounding boxes. | |
| [[https://www.mathworks.com/help/vision/examples/detecting-cars-using-gaussian-mixture-models.html|Extracting the foreground objects]] is really performing blob extraction from the binary foreground image. | |
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| Flesh out the ''overlayState'' function so that it can overlay the surveillance system output over the current image frame. In addition to keeping track of the total number of detected objects, like in the example, for each box plotted, plot the detection index associated to the box like [[http://www.mathworks.com/help/examples/vision_product/multiObjectTracking_02.png|here]]. | |
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| Apply the algorithm to the video [[http://www.cvg.reading.ac.uk/PETS2009/a.html | S2.L1 Walking, View 001]] from the PETS 2009 People Tracking Dataset. It may be a collection of images, in which case some modification of the main loop will be needed. Also apply to the couple of videos that you've selected (here, couple = exactly two). If one of them is the S2.L1 PETS 2009 video, then select another one to process. | |
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| //Explore & Deliverables:// Turn in state overlays of the processed video for the specified PETS 2009 dataset, plus for your two additional videos chosen. Discuss the performance of the foreground detection process, as well as the detection labelling for the targets over time. Are there any challenges that pop up regarding the box estimation process? | |
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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 form of data association is known as the //assignment problem.// | |
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| Two algorithms: | |
| [[https://www.mathworks.com/matlabcentral/fileexchange/20652-hungarian-algorithm-for-linear-assignment-problems--v2-3-|Hungarian or Munkres' algorithm]] and | |
| [[https://www.mathworks.com/matlabcentral/fileexchange/26836-lapjv-jonker-volgenant-algorithm-for-linear-assignment-problem-v3-0|Jonker-Volgenant Algorithm]]. | |
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| The net result should be similar to this Matlab demo on [[http://www.mathworks.com/help/vision/examples/motion-based-multiple-object-tracking.html|multiple object tracking]], which also implements far more than what is described. You should strip the extras. | |
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| __** Week #4: Adding Temporal Dynamics via a Kalman Filter **__ \\ | |
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| 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 | |
| [[https://www.mathworks.com/help/vision/examples/using-kalman-filter-for-object-tracking.html|object tracking]] along with a more general [[https://www.mathworks.com/discovery/kalman-filter.html | intro page]] to additional documentation. | |
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| * If you are interested in the details of the Kalman filter and their connection to code, this Matlab file exchange [[https://www.mathworks.com/matlabcentral/fileexchange/5377-learning-the-kalman-filter|code tutorial]] may be of assistance. | |
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| ===== Module #2: Going Further ===== | |
| ------------------------------------ | |
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| __** Week #1: Differentiating People **__ | |
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| Gaussian mixture model for targets using [[http://www.mathworks.com/matlabcentral/fileexchange/26184-em-algorithm-for-gaussian-mixture-model|Expectation Maximization algorithm]]. | |
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| __** Week #2: Appearance-Based Data Association **__ | |
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| Comparing models. | |
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| __** Week #3: Re-Identification **__ | |
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| Using comparison to know when same person has re-entered. | |
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| ===== Module #3: Merging and Splitting ===== | |
| -------------------------- | |
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| TBD. | |
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| ===== Additional Information ===== | ===== Additional Information ===== |
