Drone-based object counting is vital due to the prevalence of drones. We propose Layout Proposal Networks (LPNs) to simultaneously countlocalize target objects (e.g., cars) in drone-view videos. The method can be extended to other valuable objects such as cows, tanks, etc. We leverage the spatial layout cues (e.g., cars often park regularly) to augment the network design. We also present a new large-scale dataset (CARPK) that contains nearly 90K cars captured from different parking lots.

To our knowledge, this is the first work that leverages spatial layout cues for drone-view object region proposal. We improve the average recall of the state-of-the-art region proposal methods (i.e., 59.9 to 62.5) on a public PUCPR dataset. We contributed the large-scale dataset (CARPK) containing more than 90K cars, the first drone-view dataset. Moreover, it out performs state-of-the-art object

The core technologies significantly improve the object detectioncounting based on the advanced convolutional neural networksoutperformed the state-of-the-art. Besides vehicles, the technology can be customized for valuable objects such as cows, pineapples, tanks, etc. It can also be extended to the medical domainreduce the tedious labeling tasks for the radiologistspathologists