Transportation Object Counting With Graph-Based Adaptive Auxiliary Learning



Meng, Yanda ORCID: 0000-0001-7344-2174, Bridge, Joshua, Zhao, Yitian, Joddrell, Martha, Qiao, Yihong, Yang, Xiaoyun, Huang, Xiaowei ORCID: 0000-0001-6267-0366 and Zheng, Yalin ORCID: 0000-0002-7873-0922
(2023) Transportation Object Counting With Graph-Based Adaptive Auxiliary Learning. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 24 (3). pp. 3422-3437.

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Abstract

This paper proposes an adaptive auxiliary task learning-based approach for transport object counting problems such as humans and vehicles. These problems are essential in many real-world tasks such as video surveillance, traffic monitoring, public security, and urban planning, to aid intelligent transportation systems. Unlike existing auxiliary task learning-based methods, we develop an attention-enhanced adaptively shared backbone network to enable both task-shared and task-tailored features that are learned in an end-to-end manner. The network seamlessly combines a standard Convolution Neural Network (CNN) and a Graph Convolution Network (GCN) for feature extraction and feature reasoning among different domains of tasks. Our approach gains enriched contextual information by iteratively and hierarchically fusing features across different task branches of the adaptive CNN backbone. The whole framework pays special attention to objects' spatial locations and varied density levels, informed by object (or crowd) segmentation and density level segmentation auxiliary tasks. In particular, thanks to the proposed dilated contrastive density loss function, our network benefits from individual and regional context supervision, along with strengthened robustness. Experiments on six challenging multi-domain datasets demonstrate that our method achieves superior performance compared with state-of-the-art auxiliary task learning-based counting methods. Our code is publicly available.

Item Type: Article
Uncontrolled Keywords: Object counting, GCN, dilated contrastive density loss, adaptive auxiliary task
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User: Symplectic Admin
Date Deposited: 01 Dec 2022 14:24
Last Modified: 15 Mar 2024 10:24
DOI: 10.1109/TITS.2022.3226504
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3166463