Multi-camera Video Surveillance: Detection, Occlusion Handling, Tracking and Event Recognition
Activity recognition for video surveillance
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![Page 1: Activity recognition for video surveillance](https://reader035.fdocuments.in/reader035/viewer/2022062307/55615d9ad8b42a87628b4731/html5/thumbnails/1.jpg)
Unusual Activity Detection
Dipankar SarkarMayank Kukreja
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Structure
• Problem Statement– Baseline Testing Framework
– Unusual Activity Detection• About Activity Recognition• Current progress• Bibliography
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Problem Statement
• The goal of the B.Tech project is to eventually detect unusual activity, the project has been divided into two phases– Setting up the framework for collection and
easy retrieval of data.
– Platform to allow unusual activity detection. Building such a module over the existing activity recognition system.
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Activity Recognition
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Crude Layer
Object Detection
..Adaptive Background Subtraction
Initial learning to get the Average image and edge image. Foreground segmentation based on adaptive thresholds.
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Background Subtraction
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Physical Layer
Body Pose Recognition.
Human Body Model Fitting.
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Human Body Poses
System trained with a set of sample images.
Nearest neighbor match gives body pose.
Bayesian Classifier for Body model fitting.
Sitting
Standing
Bending
Crawling
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Logical Layer
Occlusion Handling and Tracking
Histogram and Correlogram model associated with each object.
Correlogram for handling occlusions. Kalman tracking (linear model).
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Activity Layer• Intelligent information built based on info from logical
layer.
• “Supervised” State machine to detect events.
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Current Work
Crude Activity Layer
Pruned Activity layer
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Framework
• The framework required for collection of data has the following components– Cameras mounted at various locations
– Online functioning of the Activity Recognition application.
– Machines capturing the video streams and databases to enable easy searching of relevant information in the captured data.
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Progress
• The physical infrastructure for the collection
of data has been nearly setup.– Three network cameras mounted at various
locations and different orientations on the 3rd floor. They will be the part of a private surveillance network.
– Orders have been placed for servers which will be used for data collection.
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Progress - 2
• We have implemented a web-based activity search
application.– Input : It takes the activity log and the corresponding
videos.
– It processes the logs, and we use a MySQL database for storing the frame information
LocationActivityObject no.Video IDFrame no.
Database Table Structure
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Progress – 3
• We also display a 30 frame clip of the
selected search result.
• Advantage of this approach– MySQL - Database can be used by other
applications on different platforms for other purposes.
– Web-based App - Anyone on the private network can search the database using the application running on any one machine.
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Overview
Data Collection Server
Activity Recognition
(online/offline)
1 2 3
Cameras
Search Application
MySQL DB
OtherApps
Video
Activitylog
Users
Creation
Access
Access
Streaming Video
Streaming Video (online)
StaticVideo (offline)
Web browser
Any platform
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Progress – 4
• Motion – http://motion.sf.net– It is an application which does something
similar to what we have already implemented. It will access the video, perform motion detection (not activity recognition) and allows you to fill a database with frame wise information.
– It is installed, but we are yet to check out all the functionality.
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Work Plan
• Robust testbench.• Robust Background subtraction.• Unusual activity detection.
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Bibliography
• Activity recognition in Urban environments, Nitin Jindal & Shubham Singhal