UC Riverside Undergraduate Research Journal

Motion Based Bird Sensing Using Frame Differencing and Gaussian Mixture
Deep J. Shah
its other nearest centroids. A centroid is defined to be the
center of a motion detected object. The centroids found are
checked to see if they match the ground truth by detecting
their presence in the bounding box. A bounding box is a
rectangular region covering all the edges of the detected
object inside it. The size of this box is determined by the
size of the object that it covers. If a centroid matches, then
it is classified as a centroid inside the bounding box. If it
does not match then it is classified as a centroid outside the
bounding box as shown in Table 1.
N
Centroids inside
bounding box
(Average distance
in pixels)
Centroids outside
bounding box
(Average distance
in pixels)
1 4.0668 8.3747
2 10.9104 23.4571
3 20.3761 44.9948
4 31.8322 74.1308
5 48.9304 110.5066
Table 1. Distance from a centroid to N nearest centroids in the
frame showing presence of a bird.
For this part, we use dilation on differentiated regions
and connect them with m (m = 6 in this case) surrounding
pixels. This is done to connect pixels of the same object
which might have been left undetected due to the threshold
value being too high. Table 2 shows the number of times
a centroid is detected when the bird is present or absent in
a video sequence. The value for the number of centroids
detected when bird is absent in Table 2 does not include
the centroids that might have been detected in frames
that actually did not have any presence of bird. The final
column displays total number of centroids that are detected
in a given video sequence.
Number of occurrences
Video
Sequence
Number
Centroid
detected
when bird
present
(TP)
Centroid
detected
when bird
absent
(FP)
Centroid
undetected
when bird
present
(TN)
Total
Centroids
detected
in all
1 10 33 17 484
2 23 106 49 545
3 5 36 13 346
4 11 39 24 337
5 13 78 36 436
Table 2. Number of true and false occurrences for bird detection
using centroids.
Frames 51-53 from the video sequence number
4 are displayed in Figure 2. These images show the best
potential accuracy of the frame differencing approach in
trying to detect a moving object. The bird gets detected in
these frames but the frame also detects the location of the
bird in the previous frame. This is because when absolute
differencing is performed between two consecutive images,
the bird location in the previous frame is different from
that in the current frame. This leads the algorithm to spot
two birds in the differenced image. This can be seen in
the center and right image of Figure 2, where a bounding
box is located at the same location as bird’s location in the
previous frame.
Gaussian Mixture:
In comparison to frame differencing, we use a
conceptual approach to present the results that we obtain
for Gaussian mixture application. Figure 3 shows the
change in a pixel value over a range of 1000 frames. A
sudden dip in the pixel value around frame number 500
may represent a moving object over that pixel region and
Figure 2. Application of frame differencing for bird detection. Frames 51-53 from video sequence 4 are displayed from left to right in
order.
50 UCR Un d e r g r a d u a t e Re s e a r c h Jo u r n a l