Paper Info
Title
Exploring Tradeoffs in Accuracy, Energy and Latency of Scale Invariant Feature Transform in Wireless Camera Networks
Abstract
Advances in DSP technology create important avenues of research for embedded vision. One such avenue is the investigation of tradeoffs amongst system parameters which affect the energy, accuracy, and latency of the overall system. This paper reports work on benchmarking the performance and cost of scale invariant feature transform (SIFT) for visual classification on a Blackfin DSP processor. Through measurements and modeling of the camera sensor node, we investigate system performance (classification accuracy, latency, energy consumption) in light of image resolution, arithmetic precision, location of processing (local vs. server-side), and processor speed. A case study on counting eggs during avian nesting season is used to experimentally determine the tradeoffs of different design parameters and discuss implications to other application domains.
Year
DOI
Venue
2007
10.1109/ICDSC.2007.4357539
2007 First ACM/IEEE International Conference on Distributed Smart Cameras
Keywords
Field
DocType
embedded vision,system tradeoffs,DSP,SIFT,object recognition
Computer vision,Scale-invariant feature transform,Digital signal processing,Blackfin,Image sensor,Computer science,Latency (engineering),Real-time computing,Artificial intelligence,Energy consumption,Wireless sensor network,Clock rate
Conference
ISBN
Citations 
PageRank 
978-1-4244-1353-9
6
0.56
References 
Authors
7
7
Name
Order
Citations
PageRank
Teresa Ko1875.50
Zainul Charbiwala215012.93
Shaun Ahmadian3514.31
Mohammad H. Rahimi411614.64
Mani Srivastava5130521317.38
Stefano Soatto64967350.34
Deborah Estrin7253803646.75