Authors:
Isabella de Andrade
1
and
João Lima
1
;
2
Affiliations:
1
Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil
;
2
Departamento de Computação, Universidade Federal Rural de Pernambuco, Recife, Brazil
Keyword(s):
Tracking, Pedestrians, Neural Networks, Multiple Cameras.
Abstract:
Tracking the position of pedestrians over time through camera images is a rising computer vision research topic. In multi-camera settings, the researches are even more recent. Many solutions use supervised neural networks to solve this problem, requiring much effort to annotate the data and time spent training the network. This work aims to develop variations of pedestrian tracking algorithms, avoid the need to have annotated data and compare the results obtained through accuracy metrics. Therefore, this work proposes an approach for tracking pedestrians in 3D space in multi-camera environments using the Message Passing Neural Network framework inspired by graphs. We evaluated the solution using the WILDTRACK dataset and a generalizable detection method, reaching 77.1% of MOTA when training with data obtained by a generalizable tracking algorithm, similar to current state-of-the-art accuracy. However, our algorithm can track the pedestrians at a rate of 40 fps, excluding the detectio
n time, which is twice the most accurate competing solution.
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