A Visibility-Based Approach for Occupancy Grid Computation in Disparity Space

Perrollaz, Mathias; Yoder, John-David; Nègre, Amaury; Spalanzani, Anne; Laugier, Christian

doi:10.1109/tits.2012.2188393

Cited by 37 publications

(26 citation statements)

References 16 publications

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“…2) Compact Representations: Stereo-vision studies have made extensive use of compact representations of measured data, including occupancy grids [86], elevation maps [112], free space understanding [81], ground surface modeling [113], and dynamic stixels [85]. Compact representations serve to facilitate segmentation of the scene [113], identify obstacles [114], and reduce computational load.…”

Section: B Stereo Vision For Vehicle Detectionmentioning

confidence: 99%

“…In [117], the occupancy grid's state is inferred using a recursive estimation technique termed the sequential probability ratio test. In [86], the occupancy grid is filtered both temporally and spatially. In [140], the occupancy grid is set up in polar coordinates, and the cells are assigned depth-adaptive dimensions to model the field of view and depth resolution of the stereo rig.…”

Section: ) Motion-based Approachesmentioning

confidence: 99%

“…In [40], the GPU was used to implement real-time vehicle detection using HOG [34] features. GPU implementation was used in [86] for realtime occupancy grid computation. In [158], vehicle detection was implemented using a fusion of stereo vision and lidar on the GPU.…”

Section: Real-time Implementation and System Architecturementioning

confidence: 99%

See 2 more Smart Citations

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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Section: B Stereo Vision For Vehicle Detectionmentioning

confidence: 99%

Section: ) Motion-based Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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“…Current techniques utilize various features to detect vehicles [17], e. g. HoG features [5], [18], Haar-like features [19], [20], edge features [21] and optical flow [22], [23], [24].…”

Section: A Vehicle Detectionmentioning

confidence: 99%

Vehicle detection based on LiDAR and camera fusion

Zhang

Clarke

Knoll

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Abstract-Vehicle detection is important for advanced driver assistance systems (ADAS). Both LiDAR and cameras are often used. LiDAR provides excellent range information but with limits to object identification; on the other hand, the camera allows for better recognition but with limits to the high resolution range information. This paper presents a sensor fusion based vehicle detection approach by fusing information from both LiDAR and cameras. The proposed approach is based on two components: a hypothesis generation phase to generate positions that potential represent vehicles and a hypothesis verification phase to classify the corresponding objects. Hypothesis generation is achieved using the stereo camera while verification is achieved using the LiDAR. The main contribution is that the complementary advantages of two sensors are utilized, with the goal of vehicle detection. The proposed approach leads to an enhanced detection performance; in addition, maintains tolerable false alarm rates compared to vision based classifiers.Experimental results suggest a performance which is broadly comparable to the current state of the art, albeit with reduced false alarm rate.

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“…The idea is to perform features tracking in the monocular image plane of one of the stereo cameras and 3-D localization in the disparity and depth maps. The second axis deals with the transformation of the disparity map in a more reprensentative and compact form including occupancy grid [5] and ground surface modeling [6]. These different transformations aim mainly to facilitate scene segmentation and reduce compution time.…”

Section: Introductionmentioning

confidence: 99%

Vehicles Detection in Stereo Vision Based on Disparity Map Segmentation and Objects Classification

Dekkiche

Vincke

Merigot

2015

Advances in Visual Computing

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Abstract. This paper presents a coarse to fine approach of on-road vehicles detection and distance estimation based on the disparity map segmentation supervised by stereo vision. Scene segmentation is first performed relying on the robustness of the UV-disparity maps to generate free space and obstacles space. This last is investigated for on-road vehicles detection. The detection process starts with off-road objects substraction based on the connected component labeling algorithm which is also used for on-road segments extraction instead of the traditional hough transform for more robust, precise and fast detection. Objects classification is then applied to the on-road segments by using some cues describing the geometry of vehicles like width, height. However, these latter have been measured not in meter but rather in pixels in function of the disparity. The whole approach is presented and the experimental results of evaluation are shown.

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A Visibility-Based Approach for Occupancy Grid Computation in Disparity Space

Cited by 37 publications

References 16 publications

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Vehicle detection based on LiDAR and camera fusion

Vehicles Detection in Stereo Vision Based on Disparity Map Segmentation and Objects Classification

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