Abstract:
Automatic detection of weapons is significant for improving security and
well being of individuals, nonetheless, it is a difficult task due to large variety of size, shape and appearance of weapons. View point variations and
occlusion also are reasons which makes this task more difficult. Horizontal
object detection methods have greater background information along with
object thus it will have less foreground to background ratio. While Oriented aware methods will have very less background information because
it will be aligned according to object width and height, thus it will have
greater foreground to background ratio. Classification accuracy of object
detection will also improve using oriented aware methods because of greater
foreground to background ratio. The current object detection algorithms
process rectangular areas, however a slender and long rifle may really cover
just a little portion of area and the rest may contain unessential details.
To overcome these problems we present two approaches for oriented aware
weapon detection in visual data. One is using classification of angle and
other is using regression of angle. Our architecture is inspired by Faster
R-CNN. For angle classification approach we divided angle into 8 different
classes. Then we trained another parameter angle in Faster R-CNN and soft
max loss is used for angle classification. Now at the end of Faster R-CNN
we got one extra parameter which is angle class. Then at inference time
we use Linear transformations to get oriented bounding box. For angle regression technique every thing goes same except now we take original angle
as input and trained angle parameter using smooth L1 loss function. We
have trained both approaches on our own created OAWD data set. OAWD
dataset has 6400 manually annotated images which has oriented bounding
box as ground truth. Results on both approaches shows that it gives better
performance than horizontal detection of bounding box. We achieved 1%
and 0.8% improvement in mean average precision on proposed two models
than base model Faster R-CNN which is 72.98%.
