Visuals captured by high-flying aerial drones are increasingly used to assess biodiversity and animal population dynamics around the globe. Yet, challenging acquisition scenarios and tiny animal depictions in airborne imagery, despite ultra-high resolution cameras, have so far been limiting factors for applying computer vision detectors successfully with high confidence. In this paper, we address the problem for the first time by combining deep object detectors with super-resolution techniques and altitude data. In particular, we show that the integration of a holistic attention network based super-resolution approach and a custom-built altitude data exploitation network into standard recognition pipelines can considerably increase the detection efficacy in real-world settings. We evaluate the system on two public, large aerial-capture animal datasets, SAVMAP and AED. We find that the proposed approach can consistently improve over ablated baselines and the state-of-the-art performance for both datasets. In addition, we provide a systematic analysis of the relationship between animal resolution and detection performance. We conclude that super-resolution and altitude knowledge exploitation techniques can significantly increase benchmarks across settings and, thus, should be used routinely when detecting minutely resolved animals in aerial imagery.
|Number of pages||11|
|Publication status||Accepted/In press - 10 Nov 2021|
|Event||Real-World Surveillance: Applications and Challenges Workshop (RWS) at IEEE Winter Conference on Applications of Computer Vision (WACVW) - Hawaii, United States|
Duration: 8 Jan 2022 → 8 Jan 2022
|Conference||Real-World Surveillance: Applications and Challenges Workshop (RWS) at IEEE Winter Conference on Applications of Computer Vision (WACVW)|
|Period||8/01/22 → 8/01/22|