Modeling The Detection Capability Of High-Speed Spiking Cameras

• Main Authors: Zhao, Junwei, Yu, Zhaofei, Ma, Lei, Ding, Ziluo, Zhang, Shiliang, Tian, Yonghong, Huang, Tiejun
• Format: Conference Proceeding
• Language: English
• Subjects: Brightness; Cameras; Emerging Multimedia Applications; Neuromorphic Vision Sensing; Neuromorphics; Robot vision systems; Sensors; Signal processing; Signal processing algorithms; Spike Signal Processing
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP43922.2022.9747018

The novel working principle enables spiking cameras to capture high-speed moving objects. However, the applications of spiking cameras can be affected by many factors, such as brightness intensity, detectable distance, and the maximum speed of moving targets. Improper settings such as weak ambient brightness and too short object-camera distance, will lead to failure in the application of such cameras. To address the issue, this paper proposes a modeling algorithm that studies the detection capability of spiking cameras. The algorithm deduces the maximum detectable speed of spiking cameras corresponding to different scenario settings (e.g., brightness intensity, camera lens, and object-camera distance) based on the basic technical parameters of cameras (e.g., pixel size, spatial and temporal resolution). Thereby, the proper camera settings for various applications can be determined. Extensive experiments verify the effectiveness of the modeling algorithm. To our best knowledge, it is the first work to investigate the detection capability of spiking cameras.