UWE Bristol Engineering showcase 2015
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Baxter Wynnes<br />
Beng(Robotics)<br />
Project Supervisor<br />
Dr Abdul Farooq<br />
Pedestrian Detection and Motion Tracking<br />
This report discusses a solution for visually detecting and tracking moving objects in real time from a stationary camera, using humans as a test case. This<br />
is achieved by using a Kalman filter to estimate the linear trajectories of single targets that have been classified from segmented moving regions in an<br />
image sequence acquired during live acquisition. The final classifier is trained using the histograms of orientated gradients (HOG) extracted from positive<br />
and negative training images sampled from the INRIA upright pedestrian database. The author trains a Support Vector Machine to recognise HOG<br />
descriptors of people. Motion is detected by using a Gaussian Mixture Model for background subtraction, and the segmented regions are classified over<br />
different scales to test for the presence of a person. The author implements the final system design and simulation in the mathematical software package<br />
MATLAB®.<br />
Project summary<br />
A solution is produced for visually detecting and<br />
tracking moving objects in real time from a stationary<br />
camera, using humans as a test case. Achieved by<br />
using a Kalman filter to estimate the linear<br />
trajectories of single targets that have been classified<br />
from segmented moving regions in an image<br />
sequence.<br />
Project Objectives<br />
Using the aid of currently available computer vision<br />
techniques explore the area of object detection and<br />
motion tracking, the final output should have to<br />
potential to serve as a sensory input for an<br />
autonomous agent.<br />
Project Conclusion<br />
A method of pedestrian detection and motion<br />
tracking has been successfully produced using<br />
MATLAB®.<br />
The system manages to successfully perform<br />
unfiltered tracking on multiple unobstructed<br />
people with non-linear motion trajectories at<br />
a range of different scales. It can also<br />
successfully estimate the linear motion<br />
trajectory of a single target under occlusion -<br />
provided that it has been seen prior to<br />
occlusion.<br />
Step 1 : Segment Moving regions<br />
Identify whether or not a moving<br />
object is present within the current<br />
frame. To detect foreground objects<br />
we must compute a difference in<br />
values between the current scene<br />
and a background model of the<br />
scene.<br />
Step 2: Classify<br />
The next step is to classify<br />
detected regions as pedestrian<br />
or non- pedestrian. A support<br />
vector machine is trained using<br />
the histogram of orientated<br />
gradients.<br />
Step 3: Predict<br />
If a pedestrian becomes occluded,<br />
use the Kalman filter to estimate<br />
the location.<br />
Step 4: Analyse tracking sequence<br />
The final tracking system can be seen to be<br />
highly accurate when detection can be made<br />
at regular intervals , but suffers from latent<br />
detections. The Kalman filter serves a good<br />
linear estimator and succeeds in smoothing<br />
the trajectory when the interval between<br />
latent detections is small .
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