Extended target tracking utilizing machine-learning software -- with
  applications to animal classification
Magnus Malmström Anton Kullberg Isaac Skog Daniel Axehill Fredrik Gustafsson
This paper considers the problem of detecting and tracking objects in a
sequence of images. The problem is formulated in a filtering framework, using
the output of object-detection algorithms as measurements. An extension to the
filtering formulation is proposed that incorporates class information from the
previous frame to robustify the classification, even if the object-detection
algorithm outputs an incorrect prediction. Further, the properties of the
object-detection algorithm are exploited to quantify the uncertainty of the
bounding box detection in each frame. The complete filtering method is
evaluated on camera trap images of the four large Swedish carnivores, bear,
lynx, wolf, and wolverine. The experiments show that the class tracking
formulation leads to a more robust classification.