Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association

Bing Wang, Li Wang, Bing Shuai, Zhen Zuo, Ting Liu, Kap Luk Chan, Gang Wang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 1-8

Abstract


In this paper, we study the challenging problem of multi-object tracking in a complex scene captured by a single camera. Different from the existing tracklet association-based tracking methods, we propose a novel and efficient way to obtain discriminative appearance-based tracklet affinity models. Our proposed method jointly learns the convolutional neural networks (CNNs) and temporally constrained metrics. In our method, a siamese convolutional neural network (CNN) is first pre-trained on the auxiliary data. Then the siamese CNN and temporally constrained metrics are jointly learned online to construct the appearance-based tracklet affinity models. The proposed method can jointly learn the hierarchical deep features and temporally constrained segment-wise metrics under a unified framework. For reliable association between tracklets, a novel loss function incorporating temporally constrained multi-task learning mechanism is proposed. By employing the proposed method, tracklet association can be accomplished even in challenging situations. Moreover, a large-scale dataset with 40 fully annotated sequences is created to facilitate the tracking evaluation. Experimental results on five public datasets and the new large-scale dataset show that our method outperforms several state-of-the-art approaches in multi-object tracking.

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[bibtex]
@InProceedings{Wang_2016_CVPR_Workshops,
author = {Wang, Bing and Wang, Li and Shuai, Bing and Zuo, Zhen and Liu, Ting and Luk Chan, Kap and Wang, Gang},
title = {Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}