TRACKING HUMANS USING MULTIPLEPAIRS OF PTZF CAMERAS ANDWIDE-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.

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

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. Krithika Venkataramani.

BACKGROUND SUBTRACTION AND CONTOUR EVALUATION

Original Frame Fore-ground

Tracked Object with contour drawn

CAMSHIFT TRACKING It is based on the photometric cues of the image

frame.

Taking color sample

Histogram of the selected part

IMPROVED TRACKING

Original Frame

Fore-ground

Masked Frame

Apply Camshift on each part

Confidence Evaluation

Divided image frame

Tracked aligned parts

Confidence Evaluation

Histogram (Frame1)

Histogram (Current-Frame )

Cross- Correlation

Real no. [0,1]

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

where,

zk: Measurementxk: stateuk: control inputwk: process noisevk: measurement noiseF: transfer matrix

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.

xk = State of the model(after kth

update)

zk = kth measurement of parameters

CURRENT PROGRESS ON FULL OCCLUSION

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 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