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

Dekkiche, Djamila; Vincke, Bastien; Merigot, A.

doi:10.1007/978-3-319-27857-5_68

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…In [ 115 ], the UV-disparity map and DCNN were combined to jointly extract vehicle ROIs. In [ 116 ], V-disparity maps with Hough transform were first utilized to extract road areas, and then a U-disparity map was used to generate ROIs, in addition, the distance of the vehicles was also derived based on depth information. In [ 118 ], a stereo camera was fused with millimeter-wave radar for vehicle detection, where stereo images were acquired to detect nearby vehicles through UV-disparity maps.…”

Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail.

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Section: Vehicle Detection: Vision-based Methodsmentioning

confidence: 99%

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Liu

Yuan

et al. 2021

Sensors

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“…Moreover, the proposed algorithm is also claimed to recover from erroneous initial seeds [25]. A novel approach for stereo matching problem which involves the use of a binary cost estimation and aggregation method was proposed [26]. The construction of the cost volume is based on bitwise operations on a set of binary strings.…”

Section: Related Workmentioning

confidence: 99%

“…24. The state estimation at time 𝑡 + ∆𝑡 is acquired from the data from the previous instance of time t can be calculated as shown in (26) [82].…”

Section: B Segmentation Of Obstacle Regions and Path Planningmentioning

confidence: 99%

Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control

Shetty,

Hegde,

Vaz

et al. 2023

Journal of Robotics and Control

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Stereo matching algorithms work with multiple images of a scene, taken from two viewpoints, to generate depth information. Authors usually use a single matching function to generate similarity between corresponding regions in the images. In the present research, the authors have considered a combination of multiple data costs for disparity generation. Disparity maps generated from stereo images tend to have noisy sections. The presented research work is related to a methodology to refine such disparity maps such that they can be further processed to detect obstacle regions. A novel entropy based selective refinement (ESR) technique is proposed to refine the initial disparity map. The information from both the left disparity and right disparity maps are used for this refinement technique. For every disparity map, block wise entropy is calculated. The average entropy values of the corresponding positions in the disparity maps are compared. If the variation between these entropy values exceeds a threshold, then the corresponding disparity value is replaced with the mean disparity of the block with lower entropy. The results of this refinement are compared with similar methods and was observed to be better. Furthermore, in this research work, the v-disparity values are used to highlight the road surface in the disparity map. The regions belonging to the sky are removed through HSV based segmentation. The remaining regions which are our ROIs, are refined through a u-disparity area-based technique. Based on this, the closest obstacles are detected through the use of k-means segmentation. The segmented regions are further refined through a u-disparity image information-based technique and used as masks to highlight obstacle regions in the disparity maps. This information is used in conjunction with a kalman filter based path planning algorithm to guide a mobile robot from a source location to a destination location while also avoiding any obstacle detected in its path. A stereo camera setup was built and the performance of the algorithm on local real-life images, captured through the cameras, was observed. The evaluation of the proposed methodologies was carried out using real life out door images obtained from KITTI dataset and images with radiometric variations from Middlebury stereo dataset.

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Dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving

Yuan

Jiang

et al. 2023

Sci Rep

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As a core step of obstacle avoidance and path planning, dynamic obstacle detection is critical for autonomous driving. This study aimed to propose a dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving. First, a drivable area of an unmanned vehicle was detected using U–V disparity images. Then, obstacles in the drivable area were detected using U–V disparity images and the geometric relationship between obstacle size and its disparity. Finally, the motion likelihood of each obstacle was estimated by compensating the camera ego-motion. The innovation of the proposed method was that the searching range of the moving obstacles was greatly narrowed by detecting the obstacles in the drivable area, which greatly improved not only the moving obstacle detection efficiency but also the detection accuracy. Datasets from the KITTI benchmark and our self-acquired campus scene data were chosen as testing samples. The experimental results showed that our method could achieve high detection precision, low missed detection rate and less time consumption.

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

Cited by 3 publications

References 17 publications

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

Multi Cost Function Fuzzy Stereo Matching Algorithm for Object Detection and Robot Motion Control

Dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving

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