Analysis of low-altitude aerial sequences for road traffic diagnosis using graph partitioning and Markov hierarchical models

Kaâniche, Khaled; Demonceaux, Cédric; Vasseur, Pascal

doi:10.1109/ssd.2016.7473665

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…In the last few years it has made a remarkable leap. Subjects related to stereoscopic vision, such as scene analysis [5], feature descriptors [6], learning [7], and reducing processing time [8], are still under consideration.…”

Section: Introductionmentioning

confidence: 99%

Extending the stixel world using polynomial ground manifold approximation

Saleem

Rezaei

Klette

2017

2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

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Stixel-based segmentation is specifically designed towards obstacle detection which combines road surface estimation in traffic scenes, stixel calculations, and stixel clustering. Stixels are defined by observed height above road surface. Road surfaces (ground manifolds) are represented by using an occupancy grid map. Stixel-based segmentation may improve the accuracy of real-time obstacle detection, especially if adaptive to changes in ground manifolds (e.g. with respect to non-planar road geometry). In this paper, we propose the use of a polynomial curve fitting algorithm based on the v-disparity space for ground manifold estimation. This is beneficial for two reasons. First, the coordinate space has inherently finite boundaries, which is useful when working with probability densities. Second, it leads to reduced computation time. We combine height segmentation and improved ground manifold algorithms together for stixel extraction. Our experimental results show a significant improvement in the accuracy of the ground manifold detection (an 8% improvement) compared to occupancy-grid mapping methods.

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Section: Introductionmentioning

confidence: 99%

Extending the stixel world using polynomial ground manifold approximation

Saleem

Rezaei

Klette

2017

2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

View full text Add to dashboard Cite

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“…In particular, stereo vision contributes to systems that aim at distance measurements, surface modelling, or object detection [2]. This is important, for example, for scene analysis [3], feature descriptors [4], optimising learning time [5], or for reducing processing efforts in general [6].…”

Section: Introductionmentioning

confidence: 99%

“…1), multiple independent techniques may have to be cascaded. 3 These may include mapping disparities into occu- Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Improved Stixel Estimation Based on Transitivity Analysis in Disparity Space

Saleem

Chien

Rezaei

et al. 2017

Computer Analysis of Images and Patterns

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We present a novel method for stixel construction using a calibrated collinear trinocular vision system. Our method takes three conjugate stereo images at the same time to measure the consistency of disparity values by means of the transitivity error in disparity space. Unlike previous stixel estimation methods that are built based on a single disparity map, our proposed method introduces a multi-map fusion technique to obtain more robust stixel calculations. We apply a polynomial curve fitting approach to detect an accurate road manifold, using the v-disparity space which is built based on a confidence map. Comparing the depth information from the extracted stixels (using stixel maps) with depth measurements obtained from a highly accurate LiDAR range sensor, we evaluate the accuracy of the proposed method. Experimental results indicate a significant improvement of 13.6% in the accuracy of stixel detection compared to conventional binocular vision.

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