Graph-based Asynchronous Event Processing for Rapid Object Recognition

Different from traditional video cameras, event cam- eras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes. In this paper, we introduce a novel graph-based framework for event cameras, namely SlideGCN. Unlike some...

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Bibliographic Details
Main Authors: Li, Yijin, Zhou, Han, Yang, Bangbang, Zhang, Ye, Cui, Zhaopeng, Bao, Hujun, Zhang, Guofeng
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Computational complexity
Convolution
Object recognition
Prediction algorithms
Recognition and classification
Streaming media
Windows
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Summary:Different from traditional video cameras, event cam- eras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes. In this paper, we introduce a novel graph-based framework for event cameras, namely SlideGCN. Unlike some recent graph-based methods that use groups of events as input, our approach can efficiently process data event-by-event, unlock the low latency nature of events data while still maintaining the graph's structure internally. For fast graph construction, we develop a radius search algorithm, which better exploits the partial regular structure of event cloud against k-d tree based generic methods. Experiments show that our method reduces the computational complexity up to 100 times with respect to current graph-based methods while keeping state-of-the-art performance on object recognition. Moreover, we verify the superiority of event-wise processing with our method. When the state becomes stable, we can give a prediction with high confidence, thus making an early recognition.
ISSN:2380-7504
DOI:10.1109/ICCV48922.2021.00097