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While generic object detection has achieved large improvements with richfeature hierarchies from deep nets, detecting small objects with poor visualcues remains challenging. Motion cues from multiple frames may be moreinformative for detecting such hard-to-distinguish objects in each frame.However, how to encode discriminative motion patterns, such as deformations andpose changes that characterize objects, has remained an open question. To learnthem and thereby realize small object detection, we present a neural modelcalled the Recurrent Correlational Network, where detection and tracking arejointly performed over a multi-frame representation learned through a single,trainable, and end-to-end network. A convolutional long short-term memorynetwork is utilized for learning informative appearance change for detection,while learned representation is shared in tracking for enhancing itsperformance. In experiments with datasets containing images of scenes withsmall flying objects, such as birds and unmanned aerial vehicles, the proposedmethod yielded consistent improvements in detection performance over deepsingle-frame detectors and existing motion-based detectors. Furthermore, ournetwork performs as well as state-of-the-art generic object trackers when itwas evaluated as a tracker on the bird dataset.

Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects