Presentation at IJCNN within WCCI 2022

1. Crowd Flow Detection from Drones
with Fully Convolutional Networks
and Clustering
Giovanna Castellano, Corrado Mencar, Gaetano Sette,
Francesco Saverio Troccoli, Gennaro Vessio
Department of Computer Science, University of Bari, Italy
IEEE WCCI
2022

2. Context
More and more people live in urban areas
Gatherings of people (political rallies, festival
celebrations, concerts, …) pose serious challenges
to urban security and management
In this perspective, automated crowd analysis
methods have attracted increasing attention

3. Motivations
A cost-effective way to perform automated crowd
analysis is the use of drones
Indeed, once equipped with inexpensive but
powerful enough cameras and GPUs, drones can
become flying computer vision devices
However, while this perspective is fascinating, there
are also several drawbacks to be aware of…

4. Goal
There is a large body of knowledge on crowd
counting and crowd density estimation, but none
on crowd flow detection
This poses a new challenge as the goal is not only to
estimate the number of people but also to
determine how the crowd flows
Our goal is to try to fill this gap

5. Proposed method

6. Proposed method
Input: consecutive frames taken by a drone
separated by any time interval

7. Proposed method
MobileCount
is used to
generate
crowd
density maps

8. Proposed method
The density maps are
then thresholded and
provided as input to a
clustering module

9. Proposed method The groups of people are
identified by their centroids
and the shift of the centroids
is used to determine their
direction of movement

10. VisDrone Crowd Counting 2020 dataset
❏ 112 sequences (82 + 30) with 1920x1080 resolution
❏ 70 different scenarios in China
❏ People head annotations

11. Performance metrics
Density estimation:
❏ MAE
❏ RMSE
Clustering:
❏ Silhouette coefficient
❏ Davies-Bouldin index
❏ Mean Coordinate Matching Error (MCME)

12. Results: density estimation
MAE RMSE

13. Results: clustering
SC DB
MCME

14. Conclusion and future work
The joint exploitation of crowd density estimation
and clustering has provided promising results
A future development could be to integrate the
clustering task directly into a deep learning model
trained to perform density estimation and
clustering simultaneously*
The overall inference time, in fact, still needs to be
improved… 😔
*Spoiler: in the meantime, we have already obtained
promising results in this direction 😉
TIME (Google Colab)

15. Thanks for the attention!
gennaro.vessio@uniba.it