Retrospective convolution and static sample synthesis for instantaneous change detection

Chao Chen; Sheng Zhang; Cuibing Du

doi:10.1117/12.2540999

31 July 2019 Retrospective convolution and static sample synthesis for instantaneous change detection

Chao Chen, Sheng Zhang, Cuibing Du

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 111980S (2019) https://doi.org/10.1117/12.2540999
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

Abstract

Change detection has been a challenging visual task due to the dynamic nature of real-world scenes. Good performance of existing methods depends largely on prior background images or a long-term observation. These methods, however, suffer severe degradation when they are applied to detection of instantaneously occurred changes with only a few preceding frames provided. In this paper, we exploit spatio-temporal convolutional networks to address this challenge, and propose a novel retrospective convolution, which features efficient change information extraction between the current frame and frames from historical observation. To address the problem of foreground-specific overfitting in learning-based methods, we further propose a data augmentation method, named static sample synthesis, to guide the network to focus on learning change-cued information rather than specific spatial features of foreground. Trained end-to-end with complex scenarios, our framework proves to be accurate in detecting instantaneous changes and robust in combating diverse noises. Extensive experiments demonstrate that our proposed method significantly outperforms existing methods.

Citation Download Citation

Chao Chen, Sheng Zhang, and Cuibing Du "Retrospective convolution and static sample synthesis for instantaneous change detection", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980S (31 July 2019); https://doi.org/10.1117/12.2540999

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