Power Efficient Object Detector with an Event-Driven Camera for Moving Object Surveillance on an FPGA

Masayuki SHIMODA  Shimpei SATO  Hiroki NAKAHARA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.5   pp.1020-1028
Publication Date: 2019/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018RCP0005
Type of Manuscript: Special Section PAPER (Special Section on Reconfigurable Systems)
Category: Applications
Keyword: 
event-driven camera,  object detector,  all binarized convolutional neural network,  FPGA,  

Full Text: PDF(1.5MB)>>
Buy this Article




Summary: 
We propose an object detector using a sliding window method for an event-driven camera which outputs a subtracted frame (usually a binary value) when changes are detected in captured images. Since sliding window skips unchanged portions of the output, the number of target object area candidates decreases dramatically, which means that our system operates faster and with lower power consumption than a system using a straightforward sliding window approach. Since the event-driven camera output consists of binary precision frames, an all binarized convolutional neural network (ABCNN) can be available, which means that it allows all convolutional layers to share the same binarized convolutional circuit, thereby reducing the area requirement. We implemented our proposed method on the Xilinx Inc. Zedboard and then evaluated it using the PETS 2009 dataset. The results showed that our system outperformed BCNN system from the viewpoint of detection performance, hardware requirement, and computation time. Also, we showed that FPGA is an ideal method for our system than mobile GPU. From these results, our proposed system is more suitable for the embedded systems based on stationary cameras (such as security cameras).