Abstract
Detection and tracking of multiple objects in real applications requires real-time performance, the management of tens of simultaneous objects, and handling frequent partial and total occlusions. Moreover, due to the software and hardware requirements of the different algorithms, this kind of systems require a distributed architecture to run in real-time. In this paper, we propose a vision based tracking system with three components: detection, tracking and data association. Tracking is based on a Discriminative Correlation Filter combined with a Kalman filter for occlusions handling. Also, our data association uses deep features to improve robustness. The complete system runs in real-time with tens of simultaneous objects, taking into account the runtimes of the Convolutional Neural Network detector, the tracking and the data association.
This research was partially funded by the Spanish Ministry of Economy and Competitiveness under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32 (MICINN/FEDER), and the Galician Ministry of Education, Culture and Universities under grant ED431G/08. Mauro Fernández is supported by the Spanish Ministry of Economy and Competitiveness under grant BES-2015-071889. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program). We thank Dirección General de Tráfico (DGT) for their collaboration.
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Fernández-Sanjurjo, M., Mucientes, M., Brea, V.M. (2019). A Real-Time Processing Stand-Alone Multiple Object Visual Tracking System. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_6
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