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Human activity recognition

Date post:19-Nov-2014
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  • 1. ByGadamSrikanthK.SudarshanR.GopinathGuided by Dr. MaheshKumar H.Kolekar1

2. Object-level understandingLocations of persons and objects.E.g:Car appeared in the video Tracking-level understandingObject trajectories correspondence Activity-level understandingE.g: Recognition of human activities and events2 3. Human activity recognition is an important area ofcomputer vision research and applications. The goal of the activity recognition is an automatedanalysis or interpretation of ongoing events and theircontext from video data. Its applications include surveillance systems, patientmonitoring systems, and a variety of systems thatinvolve interactions between persons and electronicdevices such as human-computer interfaces. Most of these applications require recognition of high-level activities, often composed of multiple simpleactions of persons.3 4. Categorized based on their complexity: Actions: single actor movements. e.g.: bending, walking etc. Interactions: human-human/objectinteractions. e.g.: punching, lifting bag etc. Group activities: activities of groups. e.g.: group dancing, group stealing etc.4 5. Surveillance: cameras installed in areas thatmay need monitoring such asbanks, airports, military installations, andconvenience stores.Currently ,surveillance systems are mainly forrecording.The Aim for activity detection using CCTVsis to monitor suspicious activities for real-timereactions like fighting and stealing.5 6. Sports play analysis: analyzing the play and deducing the actions in the sport.e.g.:6 7. Unmanned Aerial Vehicles(UAVs):Automated understanding of aerial images.Recognition of military activities like bordersecurity, people in bunkers etc. UAV capturing 3 Taliban insurgents planting IED.(improvised explosive device) 7 8. The kit we are using for the processing of thevideo is the DEV8000 kit. It has an TI OMAP3530 processor based on600Mhz ARM cortex A8 core. Memory supporting up to 256MB DDRSDRAM and 256MB NAND flash. It even supports Ethernet , Audio , USB OTG, SD/MMC , Keyboard , UART(UniversalAsynchronous Receiver/Transmitter) , Camera, Wi-Fi , GPRS , GPS through modules . 8 9. The device includes state-of-the-art power-management techniques required for high-performance mobile products and supportshigh-level operating systems such as WindowsCE, Linux, Symbian OS , Android. The board has two methods to boot the systemfrom either SD card or NAND flash. 9 10. Autonomous All Terrain Vehicle:Build an autonomous ground vehicle in amodular way employing sensor fusion atvarious levels leading to software APIs forseveral sensors, an Attitude & HeadingReference System, a path planner and a mapbuilder. Real time images for radar and micro airvehicles:A computer-vision platform for micro airvehicles.10 11. Unmanned Aerial Vehicle with Real-Timewireless video transmission capability:The UAV will transmit video captured by itssensor to a base station in real-time. HDD based Multimedia system with video &audio:A multimedia system based on OMAP andLinux.11 12. Car Assisting System with Image and Locationprocessing:A car management system to assist driver byproviding model for outer environment withsupport of cameras, gps and other sensors. Autonomous-Seeway:The project is to make autonomous a seewayalready built, and implement their controlalgorithms for tracking people orvehicles, through vision algorithms with acamera and a laser mapping. 12 13. x-loader is a boot strap program , to initialize the CPU. u-boot is a second level boot strap program for interacting with users andupdating the images required for OS, and leading the kernel . The latest 2.6.x kernel(interface between software and hardware) is employed and can be customized based on devkit8000. Rootfs employs open source system .It is small in capacity but powerful . 13 14. The board will be booted from the NAND flash bydefault , but can also be booted from the SD card . Using hyper terminal in windows we interfaced theLCD and the Board. Installed cross compilation environment tool inUbuntu.Cross Compilation: It is used to compile for a platformupon which it is not feasible to do the compiling, likemicrocontrollers that dont support an operatingsystems. Installed other required tools and drivers in Linux.14 15. The scope of our project is recognition ofcommon activities like walking ,clapping etc. Object level: This is the first level in the recognition. Wehave to fix our Object/Objects of Interest. In this technique in a video after acquiring thefirst frame the user manually fix some pointscalled feature points on the frame according tohuman anatomy. 15 16. The feature points are such that the partsbetween the points are rigid. We finally formthe skeleton structure of the human body. Then these points can used to form rectanglesresembling human structure now the finalstructure formed is a model on which thecomputer works on. The figure which followsillustrate this16 17. 17 18. Tracking:Once we had divided the human into rectangular segments. We can track them in following frames .And hence we can track their motion.This can be done by searching for the rectangular region which matches the original rectangular region that was in first frame and tracking it. Thus we can at any point of time keep the track of rectangular frames which help us to track the human motion as a whole.18 19. Here searching in the sense it means that findingthe region where the pixel by pixel match isvery high.Other methods of tracking can also be used whichare simple than this but the conditions underwhich they can be used may differ.Image segmentation method can also be used butcondition must be that the back ground mustbe well known. Shown next19 20. One of the techniques of Image segmentation isknown background subtraction to extract ourdesired object of interest. Once extracted tracking can be easily done aswe are able to separate image into backgroundand foreground the movement of human canbe interpreted by the movement of foregroundand thus it can be tracked. 20 21. 21 22. Activity Recognition: There are several ways to identify the activityrecognition Model Fitting: In this method the resulting pattern of motionwhich is obtained is compared with the activitytemplates which are already present in thememory. The activity is recognized by figuring out thebest match with the templates.22 23. References: Devkit 8000 user manualAbstract of Dr .Omaima Nomir ( ComputerSciences Department, Faculty of Computer andInformation, Mansoura University ) Google 23

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