Deep Learning for Video Object Detection and Tracking
Brais Bosquet, Mauro Fernández-Sanjurjo, Lorenzo Vaquero, Daniel Cores, Víctor M. Brea, Manuel Mucientes
Machine Learning Workshop Galicia
Brief summary of deep convolutional approaches for small-object detection, real-time multi-object tracking, and integrated traffic monitoring.
This workshop paper addresses object detection and object tracking in video using convolutional networks generated through deep learning. It focuses on three lines of work: small target detection for objects below 16x16 pixels with STDnet, real-time tracking of multiple objects using a convolutional network, and traffic monitoring through an integrated detection and tracking system. STDnet includes an early visual attention mechanism, the Region Context Network, which selects promising regions containing small objects and their context while keeping memory use low. The integrated detection and tracking system is designed to operate in real time with hundreds of objects and remain robust to full occlusions by combining low-level visual tracking with high-level motion modeling, data association, and tracker reinitialization.
@inproceedings{bosquet2018deep,
author = {Brais Bosquet and
Mauro Fern{\'{a}}ndez-Sanjurjo and
Lorenzo Vaquero and
Daniel Cores and
V{\'{\i}}ctor M. Brea and
Manuel Mucientes},
title = {Deep Learning para Detecci{\'{o}}n y Tracking de Objetos en
V{\'{\i}}deo},
booktitle = {Machine Learning Workshop Galicia ({WGML})},
year = {2018}
}