  We aim to detect and identify multiple objects using multiple cameras and
computer vision for disaster response drones. The major challenges are taming
detection errors, resolving ID switching and fragmentation, adapting to
multi-scale features and multiple views with global camera motion. Two simple
approaches are proposed to solve these issues. One is a fast multi-camera
system that added a tracklet association, and the other is incorporating a
high-performance detector and tracker to resolve restrictions. (...) The
accuracy of our first approach (85.71%) is slightly improved compared to our
baseline, FairMOT (85.44%) in the validation dataset. In the final results
calculated based on L2-norm error, the baseline was 48.1, while the proposed
model combination was 34.9, which is a great reduction of error by a margin of
27.4%. In the second approach, although DeepSORT only processes a quarter of
all frames due to hardware and time limitations, our model with DeepSORT
(42.9%) outperforms FairMOT (71.4%) in terms of recall. Both of our models
ranked second and third place in the `AI Grand Challenge' organized by the
Korean Ministry of Science and ICT in 2020 and 2021, respectively. The source
codes are publicly available at these URLs
(github.com/mlvlab/drone_ai_challenge, github.com/mlvlab/Drone_Task1,
github.com/mlvlab/Rony2_task3, github.com/mlvlab/Drone_task4).
