Research on vehicle detection based on the regional feature fusion

Abstract

Vehicle detection plays a crucial role in the decision-making, planning, and control of intelligent vehicles. It is one of the main tasks of environmental perception and an essential part of ensuring driving safety. In order to capture unique vehicle features and improve vehicle recognition efficiency, this paper fuses texture features of image and edge features of LIDAR to detect frontal vehicle targets. First, we use wavelet analysis and geometric analysis to segment the ground and determine the region of interest for vehicle detection. Then, the point cloud of the vehicle detected is projected into the image to locate the ROI. Moreover, the edge feature of the vehicle is guided to extract according to the maximum gradient direction of the vehicle’s rear contour. Furthermore, the Haar texture feature is integrated to identify the vehicle, and a filter is designed according to the point cloud’s spatial distribution to eliminate the error targets. Finally, it is verified by real-vehicle comparison tests that the proposed fusion method can effectively improve the vehicles’ detection with not much time.

Keywords

Ground segmentation ROI Haar texture rear contour feature fusion

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References

Fung

Chen

MZQ

Chen

. Sensor fusion: a review of methods and applications. In: 2017 29th Chinese control and decision conference (CCDC), Chongqing, China, 28–30 May 2017, pp.3853–3860. New York, NY: IEEE.

Liu

Zhou

Liu

, et al. Tunable adaptive detection in colocated MIMO Radar. IEEE Trans Signal Process 2018; 66: 1080–1092.

Nuss

Thom

Danzer

, et al. Fusion of laser and monocular camera data in object grid maps for vehicle environment perception. In: 17th international conference on information fusion (FUSION), International Society of Information Fusion, Salamanca, Spain, 7–10 July 2014, pp.1–8. New York, NY: IEEE.

Thepade

Bhondave

Mishra

. Comparing score level and feature level fusion in multimodal biometric identification using iris and palmprint traits with fractional transformed energy content. In: International conference on computational intelligence & communication networks, Jabalpur, India, 12–14 December 2015. New York, NY: IEEE.

Qiu

, et al. A feature level fusion target detection method based on dynamic fuzzy integral. In: Proceedings of 12th international conference on signal processing (ICSP), Hangzhou, China, 19–23 October 2014, pp.1367–1371. New York, NY: IEEE.

Bertozzi

Bombini

Cerri

, et al. Obstacle detection and classification fusing radar and vision. In: 2008 IEEE intelligent vehicles symposium, Eindhoven, The Netherlands, 4–6 June 2008, pp. 608–613. New York, NY: IEEE.

Xie

Yang

Jiang

, et al. Pixels and 3-D points alignment method for the fusion of camera and LiDAR data. IEEE Trans Instrum Meas 2019; 68: 3661–3676.

Zhang

Clarke

Knoll

. Vehicle detection based on LiDAR and camera fusion. In: 17th international IEEE conference on intelligent transportation systems (ITSC), Qingdao, China, 8–11 October 2014, pp.1620–1625. New York, NY: IEEE.

Kang

Kum

. Camera and Radar sensor fusion for robust vehicle localization via vehicle part localization. IEEE Access 2020; 8: 75223–75236.

10.

Han

Tai

, et al. A real-time LIDAR and vision based pedestrian detection system for unmanned ground vehicles. In: 2015 3rd IAPR Asian conference on pattern detection (ACPR), Kuala Lumpur, Malaysia, 3–6 November 2015, pp.635–639. New York, NY: IEEE.

11.

Mohammad

RAH

Yaser

Xin

, et al. Multi-sensor data fusion for vehicle detection in autonomous vehicle applications. Electron Imaging 2018; 2018(17): 257.

12.

Nguyen

Yoo

. Fusing LIDAR sensor and RGB camera for object detection in autonomous vehicle with fuzzy logic approach. In: 2021 International Conference on Information Networking (ICOIN), Jeju Island, Korea (South), 13–16 January 2021, pp.788–791. New York, NY: IEEE.

13.

Chen

Fragonara

Tsourdos

. RoIFusion: 3D object detection from LiDAR and vision. IEEE Access 2021; 9: 51710–51721.

14.

Caltagirone

Bellone

Svensson

, et al. LIDAR – camera fusion for road detection using fully convolutional neural networks. Robot Auton Syst 2019; 111: 125–131.

15.

Lekic

Babic

. Automotive radar and camera fusion using generative adversarial networks. Comput Vis Image Underst 2019; 184: 1–8.

16.

Chen

Wan

, et al. Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE conference on computer vision and pattern detection, Honolulu, HI, 21–26 July 2017, pp.1907–1915. New York, NY: IEEE.

17.

Boggess

Narcowich

. A first course in wavelets with fourier analysis. New York, NY: John Wiley & Sons, 2015.

18.

Xiang

Choi

Lin

, et al. Subcategory-aware convolutional neural networks for object proposals and detection. In: 2017 IEEE winter conference on applications of computer vision (WACV), Santa Rosa, CA, 24–31 March 2017, pp.924–933. New York, NY: IEEE.

19.

Jeong

Park

Kwak

. Enhancement of SSD by concatenating feature maps for object detection. In: British machine vision conference 2017, 4-7 september 2017, pp.76.1–76.12. the United Kindom, UK: BMVA Press.

20.

Niu

Ouyang

. Fusion strategy of multi-sensor based object detection for self-driving vehicles. In: 2020 international wireless communications and mobile computing (IWCMC), Limassol, Cyprus, 15–19 June 2020, pp.1549–1554. New York, NY: IEEE.

21.

Kang

. Object detection and classification by decision-level fusion for intelligent vehicle systems. Sensors 2017; 17: 207.