Name
Abstract | ||
|---|---|---|
We demonstrate an object detection system using a sliding window method for an event-driven camera[3] which outputs a subtracted frame (usually a binary value) when changes are detected in captured images. Fig. 1 shows the overall architecture of the object detector using a sliding window. First, our system extracts the object region candidates by applying a sliding window to the picture output by an event-driven camera. Since the event-driven camera outputs are binary, the sliding window determines whether an object exists or not based on the proportion of white pixels inside the bounding box as shown in Fig. 2. It reduces the number of proposed regions from 1,485 to average 10. The extracted pictures are resized to 40 × 40 size and applied to the ABCNN (All Binarized Convolutional Neural Network[7]), which is a BCNN (Binarized Convolutional Neural Network[4]) in which the first convolutional layer is done in binary form as well. All binarization is found to improve both the area and computation time, while the classification accuracy decreases. If the ABCNN infers the detected object as human, then it draws a box on the proposed region. Since more than one bounding box is commonly drawn against a single detected object, non-maximum suppression is used to reduce the extras to a single box. The proposed system is implemented on an FPGA and a GPU to show the comparison of them. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FPL.2018.00090 | 2018 28th International Conference on Field Programmable Logic and Applications (FPL) |
Keywords | Field | DocType |
eventdrivencamera | Object detection,Computer vision,Sliding window protocol,Convolutional neural network,Computer science,Real-time computing,Artificial intelligence,Pixel,Artificial neural network,Detector,Binary number,Minimum bounding box | Conference |
ISSN | ISBN | Citations |
1946-147X | 978-1-5386-8518-1 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masayuki Shimoda | 1 | 8 | 6.45 |
| Shimpei Sato | 2 | 43 | 13.03 |
| Hiroki Nakahara | 3 | 155 | 37.34 |