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Spatial-Importance-Based Computation Scheme for Real-Time Object Detection From 3D Sensor Data
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DOI：10.1109/ACCESS.2022.3140332.png)
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Abstract
En 中文
Three-dimensional (3D) sensor networks using multiple light-detection-and-ranging (LIDAR) sensors are good for smart monitoring of spots, such as intersections, with high potential risk of road-traffic accidents. The image sensors must share the strictly limited computation capacity of an edge computer. To have the computation speeds required from real-time applications, the system must have a short computation delay while maintaining the quality of the output, e.g., the accuracy of the object detection. This paper proposes a spatial-importance-based computation scheme that can be implemented on an edge computer of image-sensor networks composed of 3D sensors. The scheme considers regions where objects exist as more likely to be ones of higher spatial importance. It processes point-cloud data from each region according to the spatial importance of that region. By prioritizing regions with high spatial importance, it shortens the computation delay involved in the object detection. A point-cloud dataset obtained by a moving car equipped with a LIDAR unit was used to numerically evaluate the proposed scheme. The results indicate that the scheme shortens the delay in object detection.
Keywords:
Three-dimensional displays
Object detection
Point cloud compression
Image edge detection
Proposals
Monitoring
Real-time systems
Smart monitoring
object detection
LIDAR sensor
point cloud
edge computing
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W