The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Minh N. - One of the best experts on this subject based on the ideXlab platform.
-
CVPR Workshops - Traffic Flow Analysis with Multiple Adaptive Vehicle Detectors and Velocity Estimation with Landmark-Based Scanlines
2018 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018Co-Authors: Minh-triet Tran, Tung Dinh-duy, Thanh-dat Truong, Vinh Ton-that, Quoc-an Luong, Thanh-an Nguyen, Vinh-tiep Nguyen, Minh N.Abstract:In this paper, we propose our method for vehicle detection with multiple adaptive vehicle detectors and velocity estimation with landmark-based Scanlines. Inspired by the idea for tiny object detection, we use Faster R-CNN with Resnet-101 to create different specialized vehicle detectors corresponding to different levels of details and poses. We propose a heuristic to check the fitness of a particular vehicle detector to a specific region in camera's view by the mean velocity direction and the mean object size. By this way, we can determine an adaptive set of appropriate vehicle detectors for each region in camera's view. Thus our system is expected to detect vehicles with high accuracy, both in precision and recall, even with tiny objects. We exploit the U.S. road rules for the length and distance of broken white lines on roads to propose our method for vehicle's velocity estimation using such landmarks. We determine equally-distributed Scanlines, virtual parallel lines that are nearly-perpendicular to the road direction, with reference to the line connecting the corresponding ends of multiple broken white lines. From the timespan for a vehicle to cross two consecutive virtual Scanlines, we can calculate the average vehicle's velocity within that road segment. We also refine the speed estimation by detecting when a vehicle stops at a traffic light, and smooth the results with a moving average filter. Experiments on the dataset of Traffic Flow Analysis from NVIDIA AI City Challenge 2018 show that our method achieves the perfect detect rate of 100%, the average velocity difference of 6.9762 mph on freeways, and 8.9144 mph on both freeways and urban roads.
Peter Reinartz - One of the best experts on this subject based on the ideXlab platform.
-
Multi-label learning based semi-global matching forest
Remote Sensing, 2020Co-Authors: Yuanxin Xia, Pablo D'angelo, Jiaojiao Tian, Friedrich Fraundorfer, Peter ReinartzAbstract:Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other Scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently.
Minh-triet Tran - One of the best experts on this subject based on the ideXlab platform.
-
traffic flow analysis with multiple adaptive vehicle detectors and velocity estimation with landmark based Scanlines
Computer Vision and Pattern Recognition, 2018Co-Authors: Minh-triet Tran, Thanh-dat Truong, Quoc-an Luong, Thanh-an Nguyen, Tung Dinhduy, Vinh Tonthat, Vinh-tiep NguyenAbstract:In this paper, we propose our method for vehicle detection with multiple adaptive vehicle detectors and velocity estimation with landmark-based Scanlines. Inspired by the idea for tiny object detection, we use Faster R-CNN with Resnet-101 to create different specialized vehicle detectors corresponding to different levels of details and poses. We propose a heuristic to check the fitness of a particular vehicle detector to a specific region in camera's view by the mean velocity direction and the mean object size. By this way, we can determine an adaptive set of appropriate vehicle detectors for each region in camera's view. Thus our system is expected to detect vehicles with high accuracy, both in precision and recall, even with tiny objects. We exploit the U.S. road rules for the length and distance of broken white lines on roads to propose our method for vehicle's velocity estimation using such landmarks. We determine equally-distributed Scanlines, virtual parallel lines that are nearly-perpendicular to the road direction, with reference to the line connecting the corresponding ends of multiple broken white lines. From the timespan for a vehicle to cross two consecutive virtual Scanlines, we can calculate the average vehicle's velocity within that road segment. We also refine the speed estimation by detecting when a vehicle stops at a traffic light, and smooth the results with a moving average filter. Experiments on the dataset of Traffic Flow Analysis from NVIDIA AI City Challenge 2018 show that our method achieves the perfect detect rate of 100%, the average velocity difference of 6.9762 mph on freeways, and 8.9144 mph on both freeways and urban roads.
-
CVPR Workshops - Traffic Flow Analysis with Multiple Adaptive Vehicle Detectors and Velocity Estimation with Landmark-Based Scanlines
2018 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018Co-Authors: Minh-triet Tran, Tung Dinh-duy, Thanh-dat Truong, Vinh Ton-that, Quoc-an Luong, Thanh-an Nguyen, Vinh-tiep Nguyen, Minh N.Abstract:In this paper, we propose our method for vehicle detection with multiple adaptive vehicle detectors and velocity estimation with landmark-based Scanlines. Inspired by the idea for tiny object detection, we use Faster R-CNN with Resnet-101 to create different specialized vehicle detectors corresponding to different levels of details and poses. We propose a heuristic to check the fitness of a particular vehicle detector to a specific region in camera's view by the mean velocity direction and the mean object size. By this way, we can determine an adaptive set of appropriate vehicle detectors for each region in camera's view. Thus our system is expected to detect vehicles with high accuracy, both in precision and recall, even with tiny objects. We exploit the U.S. road rules for the length and distance of broken white lines on roads to propose our method for vehicle's velocity estimation using such landmarks. We determine equally-distributed Scanlines, virtual parallel lines that are nearly-perpendicular to the road direction, with reference to the line connecting the corresponding ends of multiple broken white lines. From the timespan for a vehicle to cross two consecutive virtual Scanlines, we can calculate the average vehicle's velocity within that road segment. We also refine the speed estimation by detecting when a vehicle stops at a traffic light, and smooth the results with a moving average filter. Experiments on the dataset of Traffic Flow Analysis from NVIDIA AI City Challenge 2018 show that our method achieves the perfect detect rate of 100%, the average velocity difference of 6.9762 mph on freeways, and 8.9144 mph on both freeways and urban roads.
Siddhartha Sikdar - One of the best experts on this subject based on the ideXlab platform.
-
Sparsity Analysis of a Sonomyographic Muscle–Computer Interface
IEEE Transactions on Biomedical Engineering, 2020Co-Authors: Nima Akhlaghi, Ananya Dhawan, Amir A. Khan, Biswarup Mukherjee, Guoqing Diao, Cecile Truong, Siddhartha SikdarAbstract:Objective: Sonomyography has been shown to be a promising method for decoding volitional motor intent from analysis of ultrasound images of the forearm musculature. The objectives of this paper are to determine the optimal location for ultrasound transducer placement on the anterior forearm for imaging maximum muscle deformations during different hand motions, and to investigate the effect of using a sparse set of ultrasound Scanlines for motion classification for ultrasound-based muscle-computer interfaces (MCIs). Methods: The optimal placement of the ultrasound transducer along the forearm was identified using freehand three-dimensional reconstructions of the muscle thickness during rest and motion completion. Based on the ultrasound images acquired from the optimally placed transducer, classification accuracy with equally spaced Scanlines across the cross-sectional field of view was determined. Furthermore, the unique contribution of each scanline to class discrimination using Fisher criterion (FC) and mutual information (MI) with respect to motion discriminability was determined. Results: Experiments with five able-bodied subjects show that the maximum muscle deformation occurred between 40%-50% of the forearm length for multiple degrees-of-freedom. The average classification accuracy was 94% ± 6% with the entire 128-scanline image and 94% ± 5% with four equally spaced Scanlines. However, no significant improvement in classification accuracy was observed with optimal scanline selection using FC and MI. Conclusion: For an optimally placed transducer, a small subset of ultrasound Scanlines can be used instead of a full imaging array without sacrificing performance in terms of classification accuracy for multiple degrees-of-freedom. Significance: The selection of a small subset of transducer elements can enable the reduction of computation, and simplification of the instrumentation and power consumption of wearable sonomyographic MCIs, particularly for rehabilitation and gesture recognition applications.
M. Meda - One of the best experts on this subject based on the ideXlab platform.
-
Damage zone characterization combining scan-line and scan-area analysis on a km-scale Digital Outcrop Model: The Qala Fault (Gozo)
Journal of Structural Geology, 2020Co-Authors: M. Martinelli, A. Bistacchi, S. Mittempergher, F. Bonneau, F. Balsamo, Guillaume Caumon, M. MedaAbstract:Fault damage zones can act as a preferential corridor for fluid flow in the subsurface, and for this reason the characterization of their structure, including the attributes of the associated fracture network, is fundamental. In this work, we characterize the damage zone of the Qala fault, a normal fault developed in platform carbonates of the Gozo Island (Maltese Islands). We propose a new workflow that combines scanline and scan-area analysis applied on a high resolution DOM. Linear Scanlines allow to characterize fracture spatial distribution, detect stationary area and identify damage zone width. Areal sampling permits to extract the fracture parameters matching the stationary 1D domains. This new approach allows us to: (1) univocally separate the damage zone from the background fractures, (2) identify fracture corridors, (3) collect fracture parameters (length, trend, density, intensity, spacing and topology), (4) identify the REV of the fracture density, intensity and topology and (5) characterize the fracture network connectivity.
