Hybrid neural network for event-based object tracking

Yi Huang; Yong Song; Gang Wang; Yuxin He; Yiqian Huang; Shuqi Liu; Shiqiang Wang

doi:10.1117/12.3034919

11 July 2024 Hybrid neural network for event-based object tracking

Yi Huang, Yong Song, Gang Wang, Yuxin He, Yiqian Huang, Shuqi Liu, Shiqiang Wang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321034 (2024) https://doi.org/10.1117/12.3034919
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Event stream has been used in various vision tasks due to the low latency and high dynamic range of event camera. However, because of the temporal dynamic of event stream, convolution neural networks (CNNs) are difficult to effectively extract features from event streams to achieve object tracking tasks. Besides, SNNs is suitable for processing data with temporal information because of its spiking delivery mechanism and membrane potential accumulation over time. In this work, we propose a Hybrid Neural Network (HNNet) to achieve effective event-based single object tracking tasks by combining the advantages of SNNs and Swin-Transformer. For higher feature expression ability of SNNs, we adopt the Swin-Transformer to extract features from sparse event stream. Then we use these features to modulate the threshold of SNNs neurons. What’s more, for improving tracking performance for both special and temporal features, a cross-modality fusion module is designed to fuse the two features extracted by the Swin-Transformer and SNNs. We conduct extensive experiments on three public event-based datasets (FE240, FE108, and VisEvent) and our tracker outperforms other trackers maximum at 1.1% and 6.8% in terms of area under curve (AUC) scores and precision rate respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi Huang, Yong Song, Gang Wang, Yuxin He, Yiqian Huang, Shuqi Liu, and Shiqiang Wang "Hybrid neural network for event-based object tracking", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321034 (11 July 2024); https://doi.org/10.1117/12.3034919

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KEYWORDS

Feature extraction

Transformers

Modulation

Neural networks

Neurons

Feature fusion

Data processing

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