Detection and Tracking of the Objects in Real Time Applied to the Problem of the Log Volume Estimation

1. DETECTION AND TRACKING OBJECTS IN
REAL TIME APPLIED TO THE PROBLEM OF
ROUNDWOOD ESTIMATION
Artem Kruglov, Yuriy Chiryshev1
1Ural federal university named after the first President of Russia B.N.Yeltsin

2. Preamble
The system for roundwood geometry evaluation on the
basis of machine vision allows to:
• Automate the process of the roundwood scanning and
sorting,
• Reduce an error as compared to the systems based on a
laser scanning and photocell
• Fulfill an external asessment of the quality and type of
wood
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3. Equipment
Parameter Comments
material steel
Height, min..max 1623-2400 mm
Width, min..max 1820-2200 mm
Weight 63 kg.
camera
2x Busler acA1600-
20mc
Lighting system
6 impulse projectors,
synchronized, remote
control
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4. Problems
• logs detection should be carried out in real time, which
imposes much tighter restrictions than the methods of
selecting objects on the static images:
• Two digital cameras, 20 frames per second
• Maximum processing time for each frame ~ 20 ms.
• one log is observed in a series of consecutive images, so
it is necessary to track the logs between frames in
conjunction with the detection;
• high price of an error
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5. Background model formation
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• algorithm is able to detect the
movement of shadows;
• automatic adjustment to the
periodic motions existing on the
video after a certain period of time;
• algorithm is able to reflect the slow
changes of illumination.
Nonsingular normal distribution is
calculated for each background pixel
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6. Object detection
• Implementation of morphological operations to remove
noise
• Small foreground objects and shadows are excluded from
the processing
• Connected components are combined into the blob
bounded by rectangle
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7. Matching sequential frames
• Comparison with the objects from the previous frame:
• definite correspondence between objects was found
• correspondence to the several objects of the previous frame was
found
• no correspondence was found
• Each detected object has two attributes:
• age (number of frames when object was observed)
• identity (serial number of the detected object )
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8. Object tracking
• Bayesian approach used to track the object and include
the priory and conditional probabilities of the events
during object movement:
• H1: log outside of the measuring zone;
• H2: log entering the measuring zone;
• H3: log inside of the measuring zone;
• H4: log leaving the measuring zone.
• Two criteria are considered:
• distance between the object and the zone a few frames back;
• distance between the object and the zone at the moment.
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9. Measuring zone
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• Measuring zone – area in the centre of the image where
object’s geometry could be properly measured.
• The object considered as a log passed through the measuring
system if it is associated with the following sequence of events:
1) The subject has entered the zone from top/bottom.
2) The subject has been inside the zone for a long time.
3) The object has left out the zone from the bottom/top.

10. Matching the parallel frames
Each log must has the one-to-one correspondence of its
image from the first camera to the equal image from the
second camera.
Assignment problem: the sum of the Euclidean distances
between the pair of beams of two synchronized frames
is minimized
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11. Further steps
The further analysis of the log geometry includes the
following steps:
1. Detecting the specific points in the image (Harris detector)
2. Formation the descriptors of specific points (binary descriptor
ORB)
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12. Further steps
3. Matching the descriptors (hierarchical algorithm of optical flow)
4. Filtering (RANSAC method)
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13. Conclusion
• The proposed module including algorithms for
determining the geometric parameters of logs was carried
out in MS Visual C ++ framework.
• The performance of module on the computer Intel Core i7,
2800 Mhz allows to process a video sequence with a
frequency of 20 frames per second.
• The system for roundwood geometry evaluation will be
tested in logging enterprise in July, 2016.
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14. Video
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15. Video
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16. Thank you for your attention!
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