Visual detection of micro aerial vehicles (MAVs) has received increasing
research attention in recent years due to its importance in many applications.
However, the existing approaches based on either appearance or motion features
of MAVs still face challenges when the background is complex, the MAV target is
small, or the computation resource is limited. In this paper, we propose a
global-local MAV detector that can fuse both motion and appearance features for
MAV detection under challenging conditions. This detector first searches MAV
target using a global detector and then switches to a local detector which
works in an adaptive search region to enhance accuracy and efficiency.
Additionally, a detector switcher is applied to coordinate the global and local
detectors. A new dataset is created to train and verify the effectiveness of
the proposed detector. This dataset contains more challenging scenarios that
can occur in practice. Extensive experiments on three challenging datasets show
that the proposed detector outperforms the state-of-the-art ones in terms of
detection accuracy and computational efficiency. In particular, this detector
can run with near real-time frame rate on NVIDIA Jetson NX Xavier, which
demonstrates the usefulness of our approach for real-world applications. The
dataset is available at this https URL In
addition, A video summarizing this work is available at
this https URL

全球局部微型空中获得器以独特方式结合了运动和外观特征，提高了微型空中车辆（MAV）的检测准确性，并在具有挑战性的条件下实现了高效的检测。

基于外观和运动的挑战性条件下的全球局部微型飞行器检测

Global-Local MAV Detection under Challenging Conditions based on  Appearance and Motion

Most modern multiple object tracking (MOT) systems follow the
tracking-by-detection paradigm, consisting of a detector followed by a method
for associating detections into tracks. There is a long history in tracking of
combining motion and appearance features to provide robustness to occlusions
and other challenges, but typically this comes with the trade-off of a more
complex and slower implementation. Recent successes on popular 2D tracking
benchmarks indicate that top-scores can be achieved using a state-of-the-art
detector and relatively simple associations relying on single-frame spatial
offsets -- notably outperforming contemporary methods that leverage learned
appearance features to help re-identify lost tracks. In this paper, we propose
an efficient joint detection and tracking model named DEFT, or "Detection
Embeddings for Tracking." Our approach relies on an appearance-based object
matching network jointly-learned with an underlying object detection network.
An LSTM is also added to capture motion constraints. DEFT has comparable
accuracy and speed to the top methods on 2D online tracking leaderboards while
having significant advantages in robustness when applied to more challenging
tracking data. DEFT raises the bar on the nuScenes monocular 3D tracking
challenge, more than doubling the performance of the previous top method. Code
is publicly available.

本文提出了一种名为 DEFT 的高效的联合检测和跟踪模型，它使用外观匹配网络和对象检测网络共同学习，并添加了 LSTM 来捕获运动约束，DEFT 在应用于更具挑战性的跟踪数据时具有更高的健壮性。