  This paper addresses the problem of online tracking and classification of
multiple objects in an image sequence. Our proposed solution is to first track
all objects in the scene without relying on object-specific prior knowledge,
which in other systems can take the form of hand-crafted features or user-based
track initialization. We then classify the tracked objects with a fast-learning
image classifier that is based on a shallow convolutional neural network
architecture and demonstrate that object recognition improves when this is
combined with object state information from the tracking algorithm. We argue
that by transferring the use of prior knowledge from the detection and tracking
stages to the classification stage we can design a robust, general purpose
object recognition system with the ability to detect and track a variety of
object types. We describe our biologically inspired implementation, which
adaptively learns the shape and motion of tracked objects, and apply it to the
Neovision2 Tower benchmark data set, which contains multiple object types. An
experimental evaluation demonstrates that our approach is competitive with
state-of-the-art video object recognition systems that do make use of
object-specific prior knowledge in detection and tracking, while providing
additional practical advantages by virtue of its generality.
