A Real-Time Processing Stand-Alone Multiple Object Visual Tracking System

TítuloA Real-Time Processing Stand-Alone Multiple Object Visual Tracking System
AutoresMauro Fernández-Sanjurjo, Manuel Mucientes, Vı́ctor M. Brea
TipoComunicación para congreso
Fonte The 18th International Conference on Computer Analysis of Images and Patterns, Salerno (Italia), 2019.
AbstractDetection 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. More- over, 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.
Palabras chaveMultiple object tracking, Convolutional Neural Network, Data association.