Multi-object tracking has recently become an important area of computer vision, especially for Advanced Driver Assistance Systems (ADAS). Despite growing attention, achieving high performance tracking is still challenging, with state-of-the- art systems resulting in high complexity with a large number of hyper parameters. In this paper, we focus on reducing overall system complexity and the number hyper parameters that need to be tuned to a specific environment. We introduce a novel tracking system based on similarity mapping by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance and geometric information, and is trainable end-to-end. Our system achieves competitive performance in both speed and accuracy on MOT16 challenge, compared to known state-of-the-art methods.
@article{arxiv.1609.09156,
title = {Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking},
author = {Minyoung Kim and Stefano Alletto and Luca Rigazio},
journal= {arXiv preprint arXiv:1609.09156},
year = {2017}
}
Comments
1) accepted as a poster presentation at WiML (Women in Machine Learning) workshop 2016, colocated with NIPS 2016 in Barcelona, Spain, 2) accepted as a poster presentation at MLITS (Machine Learning for Intelligent Transportation Systems) Workshop held in conjunction with the NIPS 2016 in Barcelona, Spain