  Detecting tiny objects in a high-resolution video is challenging because the
visual information is little and unreliable. Specifically, the challenge
includes very low resolution of the objects, MPEG artifacts due to compression
and a large searching area with many hard negatives. Tracking is equally
difficult because of the unreliable appearance, and the unreliable motion
estimation. Luckily, we found that by combining this two challenging tasks
together, there will be mutual benefits. Following the idea, in this paper, we
present a neural network model called the Recurrent Correlational Network,
where detection and tracking are jointly performed over a multi-frame
representation learned through a single, trainable, and end-to-end network. The
framework exploits a convolutional long short-term memory network for learning
informative appearance changes for detection, while the learned representation
is shared in tracking for enhancing its performance. In experiments with
datasets containing images of scenes with small flying objects, such as birds
and unmanned aerial vehicles, the proposed method yielded consistent
improvements in detection performance over deep single-frame detectors and
existing motion-based detectors. Furthermore, our network performs as well as
state-of-the-art generic object trackers when it was evaluated as a tracker on
a bird image dataset.
