Unusual Activity Detection

Dipankar SarkarMayank Kukreja

Structure

• Problem Statement– Baseline Testing Framework

– Unusual Activity Detection• About Activity Recognition• Current progress• Bibliography

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.

Activity Recognition

Crude Layer

Object Detection

..Adaptive Background Subtraction

Initial learning to get the Average image and edge image. Foreground segmentation based on adaptive thresholds.

Background Subtraction

Physical Layer

Body Pose Recognition.

Human Body Model Fitting.

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

Logical Layer

Occlusion Handling and Tracking

Histogram and Correlogram model associated with each object.

Correlogram for handling occlusions. Kalman tracking (linear model).

Activity Layer• Intelligent information built based on info from logical

layer.

• “Supervised” State machine to detect events.

Current Work

Crude Activity Layer

Pruned Activity layer

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.

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.

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

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.

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

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.

Work Plan

• Robust testbench.• Robust Background subtraction.• Unusual activity detection.

Bibliography

• Activity recognition in Urban environments, Nitin Jindal & Shubham Singhal