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Changdong Zhou

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2 papers

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EAAI Journal 2024 Journal Article

SOFT: Self-supervised sparse Optical Flow Transformer for video stabilization via quaternion

Naiyao Wang
Changdong Zhou
Rongfeng Zhu
Bo Zhang
Ye Wang
Hongbo Liu

Video stabilization is crucial for video representation learning, which suffers from the challenges such as the perception of unstable vision, the stripping and cognition of target motion features in complex scenes, the correction of the jittery camera systems trails. In this paper, we propose a Self-supervised sparse Optical Flow Transformer (SOFT) model, consisting of a self-supervised contrastive learning transformer network, a sparse optical flow perception network and a multimodal cognitive fusion network. The SOFT model takes advantage of optical flow to estimate motion. The sparse optical flow perception network perceiving partially sparse optical flow containing motion features. This serves as the input to the self-supervised contrastive learning transformer network for generating sparse optical flow features, which are fed into the multimodal cognitive fusion network together with the real and virtual camera pose for video frame warping. Experimental comparisons with state-of-the-art models on 4 metrics demonstrate the effectiveness of the SOFT model. It achieves the best performance with an average Stability of 0. 869 and average Distortion of 0. 993 across 6 categories videos, which shows that the SOFT model can effectively perceive the motion in the video and smooth the jitter track of videos.

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EAAI Journal 2022 Journal Article

Human trajectory forecasting using a flow-based generative model

Bo Zhang
Tao Wang
Changdong Zhou
Nicola Conci
Hongbo Liu

In this article, we present a flow-based framework for multi-modal trajectory prediction, which is able to provide an accurate and explicit inference of the latent representations on trajectory data. Differently from other typical generative models (such as GAN, VAE, etc.), the flow-based models aim at learning data distribution explicitly through an invertible network, which can convert a complicated distribution into a tractable form via invertible transformations. The whole framework is built upon the standard encoder–decoder architecture, where the LSTM is exploited as the fundamental block to capture the temporal structure of a trajectory. As a core module, we incorporate an invertible network that can learn the multi-modal distributions of trajectory data and further generate plausible future paths by sampling tricks from the standard Gaussian distribution. Extensive experiments carried out on synthetic and realistic datasets demonstrate the effectiveness of the proposed approach, and show the advantages as compared to the GAN-based and the VAE-based prediction frameworks.

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