Paper Info
Title
Accelerating FPGA-based object detection via a visual information extraction cascade
Abstract
Object detection is a major step in several computer vision applications. Recent advances in hardware acceleration for real-time object detection feature extensive use of reconfigurable hardware fabric (Field Programmable Gate Arrays -- FPGAs), and relevant research has produce quite fascinating results, in both accuracy of the detection algorithm, as well as the performance in terms of frames per second (FPS) for use in embedded systems. Detecting objects in images however, is a daunting task, and involves steps which are hardware- inefficient, both in terms of the datapath design and in terms of input/output and memory accesses. In this work, we present how a visual information extraction cascade composed of disparity estimation, edge detection and motion detection, can help in significantly reducing the data that needs to be computed. As such, it can reduce the power consumption while improving the performance of object detection algorithms. Initial results indicate data search reduction of up to 87% in the best case, with an average of more than 50%.
Year
DOI
Venue
2015
10.1145/2789116.2789147
ICDSC
Field
DocType
Citations 
Object detection,Computer vision,Datapath,Motion detection,Computer science,Edge detection,Field-programmable gate array,Real-time computing,Frame rate,Hardware acceleration,Artificial intelligence,Reconfigurable computing
Conference
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Christos Kyrkou110214.05
Theocharis Theocharides220526.83