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Embedded Computer Vision

:	Sameer Singh, Branislav Kisa?anin, Shuvra S. Bhattacharyya, Sek Chai
:	Branislav Kisačanin, Shuvra S. Bhattacharyya, Sek Chai
:	Embedded Computer Vision
:	Springer-Verlag
:	9781848003040
:	Advances in Computer Vision and Pattern Recognition
:	1
:	CHF 87.00
:

:	Anwendungs-Software
:	English

:	284
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

As a graduate student at Ohio State in the mid-1970s, I inherited a unique c- puter vision laboratory from the doctoral research of previous students. They had designed and built an early frame-grabber to deliver digitized color video from a (very large) electronic video camera on a tripod to a mini-computer (sic) with a (huge!) disk drive-about the size of four washing machines. They had also - signed a binary image array processor and programming language, complete with a user's guide, to facilitate designing software for this one-of-a-kindprocessor. The overall system enabled programmable real-time image processing at video rate for many operations. I had the whole lab to myself. I designed software that detected an object in the eldofview,trackeditsmovements nrealtime,anddisplayedarunnin description of the events in English. For example: 'An object has appeared in the upper right corner...Itismovingdownandtot eleft...Nowtheobjectisgetting loser...The object moved out of sight to the left'-about like that. The algorithms were simple, relying on a suf cient image intensity difference to separate the object from the background (a plain wall). From computer vision papers I had read, I knew that vision in general imaging conditions is much more sophisticated. But it worked, it was great fun, and I was hooked.

	Foreword	6
	Preface	8
	Embedded Computer Vision	8
	Target Audience	9
	Organization of the Book	10
	Overview of Chapters	10
	How This Book Came About	12
	Outlook	13
	Acknowledgements	14
	Contents	15
	List of Contributors	22
	Introduction	26
	Hardware Considerations for Embedded Vision Systems	27
	1.1 The Real-Time Computer Vision Pipeline	27
	1.2 Sensors	29
	1.3 Interconnects to Sensors	33
	1.4 Image Operations	35
	1.5 Hardware Components	36
	1.6 Processing Board Organization	46
	1.7 Conclusions	48
	References	49
	Design Methodology for Embedded Computer Vision Systems	51
	2.1 Introduction	51
	2.2 Algorithms	54
	2.3 Architectures	55
	2.4 Interfaces	57
	2.5 Design Methodology	59
	2.6 Conclusions	67
	References	67
	We Can Watch It for You Wholesale	72
	3.1 Introduction to Embedded Video Analytics	72
	3.2 Video Analytics Goes Down-Market	74
	3.3 How Does Video AnalyticsWork?	79
	3.4 An Embedded Video Analytics System: by the Numbers	89
	3.5 Future Directions for Embedded Video Analytics	93
	3.6 Conclusion	97
	References	98
	Advances in Embedded Computer Vision	100
	Using Robust Local Features on DSP-Based Embedded Systems	101
	4.1 Introduction	101
	4.2 RelatedWork	103
	4.3 Algorithm Selection	104
	4.4 Experiments	109
	4.5 Conclusion	119
	References	121
	Benchmarks of Low-Level Vision Algorithms for DSP, FPGA, and Mobile PC Processors	123
	5.1 Introduction	123
	5.2 RelatedWork	125
	5.3 Benchmark Metrics	125
	5.4 Implementation	126
	5.5 Results	139
	5.6 Conclusions	140
	References	141
	SAD-Based Stereo Matching Using FPGAs	143
	6.1 Introduction	143
	6.2 RelatedWork	144
	6.3 Stereo Vision Algorithm	145
	6.4 Hardware Implementation	147
	6.5 Experimental Evaluation	151
	6.6 Conclusions	159
	References	159
	Motion History Histograms for Human Action Recognition	161
	7.1 Introduction	161
	7.2 RelatedWork	163
	7.3 SVM-Based Human Action Recognition System	164
	7.4 Motion Features	165
	7.5 Dimension Reduction and Feature Combination	170
	7.6 System Evaluation	172
	7.7 FPGA Implementation on Videoware	178
	7.8 Conclusions	182
	References	183
	Embedded Real-Time Surveillance Using Multimodal Mean Background Modeling	185
	8.1 Introduction	185
	8.2 RelatedWork	186
	8.3 Multimodal Mean Background Technique	188
	8.4 Experiment	190
	8.5 Results and Evaluation	192
	8.6 Conclusion	196
	References	197
	Implementation Considerations for Automotive Vision Systems on a Fixed- Point DSP	198
	9.1 Introduction	198
	9.2 Fixed-Point Arithmetic	203
	9.3 Process of Dynamic Range Estimation	203
	9.4 Implementation Considerations for Single-Camera Steering Assistance Systems on a Fixed- Point DSP	207
	9.5 Results	211
	9.6 Conclusions	214
	References	215
	Towards OpenVL: Improving Real-Time Performance of Computer Vision Applications	216
	10.1 Introduction	216
	10.2 RelatedWork	218
	10.3 A Novel Software Architecture for OpenVL	222
	10.4 Example Application Designs	232
	10.5 Conclusion and Future Work	235
	10.6 Acknowledgements	236
	References	236
	Looking Ahead	238
	Mobile Challenges for Embedded Computer Vision	239
	11.1 Introduction	239
	11.2 In Search of the Killer Applications	241
	11.3 Technology Constraints	244
	11.4 Intangible Obstacles	250
	11.5 Future Direction	252
	References	253
	Challenges in Video Analytics	256
	12.1 Introduction	256
	12.2 Current Technology and Applications	257
	12.3 Building Blocks	263
	12.4 Embedded Implementations	267
	12.5 Future Applications and Challenges	269
	12.6 Summary	273
	References	274
	Challenges of Embedded Computer Vision in Automotive Safety Systems	276
	13.1 Computer Vision in Automotive Safety Applications	276
	13.2 Literature Review	277
	13.3 Vehicle Cueing	278
	13.4 Feature Extraction	287
	13.5 Feature Selection and Classification	293
	13.6 Experiments	295
	13.7 Conclusion	297
	References	297
	Index	299