System-platforms-based SystemC TLM design of image processing chains for embedded applications
EURASIP Journal on Embedded Systems
Computer Vision Approaches to Pedestrian Detection: Visible Spectrum Survey
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
A People Counting System Based on Dense and Close Stereovision
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
A New Approach on Spatio-temporal Scene Analysis for Driver Observation
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Dense Stereo-Based ROI Generation for Pedestrian Detection
Proceedings of the 31st DAGM Symposium on Pattern Recognition
High-Level Fusion of Depth and Intensity for Pedestrian Classification
Proceedings of the 31st DAGM Symposium on Pattern Recognition
IEEE Transactions on Intelligent Transportation Systems
Phase-Correlation Guided Search for Realtime Stereo Vision
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Estimating the driving state of oncoming vehicles from a moving platform using stereo vision
IEEE Transactions on Intelligent Transportation Systems
Real-time Quadrifocal Visual Odometry
International Journal of Robotics Research
Vision-IMU integration using a slow-frame-rate monocular vision system in an actual roadway setting
IEEE Transactions on Intelligent Transportation Systems
Mitigation of visibility loss for advanced camera-based driver assistance
IEEE Transactions on Intelligent Transportation Systems
Robotic mapping and localization with real-time dense stereo on reconfigurable hardware
International Journal of Reconfigurable Computing - Special issue on selected papers from ReconFig 2009 International conference on reconfigurable computing and FPGAs (ReconFig 2009)
Robotics and Autonomous Systems
Efficient stereo and optical flow with robust similarity measures
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Virtual worlds and active learning for human detection
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
A measure for accuracy disparity maps evaluation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Real-World stereo-analysis evaluation
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Identification of scene locations from geotagged images
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Fast implementation of dense stereo vision algorithms on a highly parallel SIMD architecture
Journal of Real-Time Image Processing
A robust cost function for stereo matching of road scenes
Pattern Recognition Letters
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Stereo vision is an attractive passive sensing technique for obtaining three-dimensional (3-D) measurements. Recent hardware advances have given rise to a new class of real-time dense disparity estimation algorithms. This paper examines their suitability for intelligent vehicle (IV) applications. In order to gain a better understanding of the performance and the computational-cost tradeoff, the authors created a framework of real-time implementations. This consists of different methodical components based on single instruction multiple data (SIMD) techniques. Furthermore, the resulting algorithmic variations are compared with other publicly available algorithms. The authors argue that existing publicly available stereo data sets are not very suitable for the IV domain. Therefore, the authors' evaluation of stereo algorithms is based on novel realistically looking simulated data as well as real data from complex urban traffic scenes. In order to facilitate future benchmarks, all data used in this paper is made publicly available. The results from this study reveal that there is a considerable influence of scene conditions on the performance of all tested algorithms. Approaches that aim for (global) search optimization are more affected by this than other approaches. The best overall performance is achieved by the proposed multiple-window algorithm, which uses local matching and a left-right check for a robust error rejection. Timing results show that the simplest of the proposed SIMD variants are more than twice as fast than the most complex one. Nevertheless, the latter still achieves real-time processing speeds, while their average accuracy is at least equal to that of publicly available non-SIMD algorithms