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Tracking Humans using Multiple pairs of PTZF Cameras and Wide-Angle Cameras. Author: Abhilash Jindal , Y7009 Brajesh Kushwaha , Y7119 Supervisor: Dr. K. S. Venkatesh Dr. Krithika Venkataramani. Aim. Identifying and tracking a VIP using 3 pairs of PTZF and wide-angle cameras.

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tracking humans using multiple pairs of ptzf cameras and wide angle cameras

Tracking Humans using Multiplepairs of PTZF Cameras andWide-Angle Cameras

Author:

AbhilashJindal, Y7009

BrajeshKushwaha, Y7119

Supervisor:

Dr. K. S. Venkatesh

Dr. KrithikaVenkataramani

slide2
Aim

Identifying and tracking a VIP using 3 pairs of PTZF and wide-angle cameras.

The final system's performance can be can be described as:-

  • Detecting all the humans in the field of view of the wide-angle cameras.
  • Targeting people one by one by the wide-angle cameras.
  • Passing the track to the PTZF camera from the corresponding wide-angle camera.
  • Simultaneous zooming of all the PTZF cameras onto each person's face.
  • Cross-checking the combined outputs of the PTZF cameras against a human face-database to recognize our VIP.
  • Tracking the identified VIP by the PTZF cameras simultaneously.
overview of the work
Overview of the work

The work has been divided into 5 parts:

  • Control of the PTZF cameras.
  • Human-Tracking using single camera.
  • Transformation of the pixels in wide-angle camera to PTZF camera.
  • Fusion of data from 3 wide-angle camera for improved tracking.
  • Recognizing individual from the output of 3 PTZF cameras.

The last part is being done as a part of a different B.Tech Project under the supervision of Dr. KrithikaVenkataramani.

background subtraction and contour evaluation
Background subtraction and Contour evaluation

Original Frame

Fore-ground

Tracked Object with contour drawn

camshift tracking
Camshift Tracking
  • It is based on the photometric cues of the image frame.

Taking color sample

Histogram of the selected part

improved tracking
Improved Tracking

Original Frame

Masked Frame

Fore-ground

slide7

Apply Camshift on each part

Confidence Evaluation

Divided image frame

Tracked aligned parts

slide8

Confidence Evaluation

Histogram (Frame1)

Real no. [0,1]

Cross- Correlation

Histogram (Current-Frame )

aligning trackers
Aligning Trackers
  • If confidence(tracker Legs) < threshold,

flag(Legs)=0;

  • If(flag(Legs)==0),

if(flag(torso)!=0)

align(Legs, torso);

else

align(Legs, Head);

Similarly for the other two trackers.

kalman filtering
Kalman Filtering

where,

zk: Measurementxk: stateuk: control input wk: process noisevk: measurement noise F: transfer matrix

slide12

where,

R: measurement error matrix / covariance of vkQ: covariance of wkP: error covariance

  • The measurement error(R) has been made inversely proportional to the confidence. An increased error ensures less importance is given to the current measurement whose confidence is low.
  • The weights in the Transfer matrix (F) have been set heuristically.
slide15

xk = State of the model

(after kth update)

zk = kth measurement of parameters

work to be done
Work to be done
  • Designing a controller for the PTZF camera for a better time-response during tracking.
  • Transforming wide-angle camera co-ordinates to the corresponding PTZF camera.
  • Extending the single camera tracking to multi-camera tracking.
references
References
  • A. Ariel, G. Mikhail, et al. Robust Real-Time background subtraction based on Local Neighborhood patterns. EURASIP Journal on Advances in Signal Processing, 2010, 2010.
  • M.D. Dixit, Combining edge and color features to track partially occluded humans, M.Tech thesis, Department of Electrical Engineering,May 2009