A Fast Evolutionary Algorithm for Real-Time Vehicle Detection

Nguyen, Vinh Dinh; Nguyen, Thuy Tuong; Nguyễn, Duc Tung; Lee, Sang Jun; Jeon, Jae Wook

doi:10.1109/tvt.2013.2242910

Cited by 35 publications

(17 citation statements)

References 38 publications

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“…We implemented the local census-based FPGA in [41] to obtain real-time processing and good results under realistic conditions. Moreover, the stereo system [41] has been used to develop a successful vehicle detection system [53]. Real-time implementation of the SGM-based LDE matching cost in an FPGA system will be considered in future work to improve the performance of the vehicle detection system.…”

Section: Discussionmentioning

confidence: 99%

Local Density Encoding for Robust Stereo Matching

Nguyen

Lee

et al. 2014

IEEE Trans. Circuits Syst. Video Technol.

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Stereo correspondence is challenging under realistic conditions due to uncontrolled factors that affect input images, including illumination inconsistencies and radiometric variations. Many local and global models have been suggested to address these problems; however, their performance is often degraded due to the assumption of color consistency between the left and right images. Therefore, we present a new local pattern, local density encoding, for stereo matching measurements to improve the performance of existing stereo methods. Our experimental results indicate that the proposed method is less sensitive to illumination changes and radiometric variations. Moreover, in cases with normal and severe illumination changes, the proposed method is more robust than state-of-the-art data costs.

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Section: Discussionmentioning

confidence: 99%

Local Density Encoding for Robust Stereo Matching

Nguyen

Lee

et al. 2014

IEEE Trans. Circuits Syst. Video Technol.

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“…For the k anchors, the anchor point is located in the centroid of the sliding window, anchors are bounding boxes that have three different sizes (128 × 128, 256 × 256, and 512 × 512) and three different aspect ratios (0.5, 1, and 2), thus k = 9, and the width and height of anchors are determined by Equation (1) W i, j = 2 s j × round( (x × y)/r i ) H i,j = 2 s j × round(round( (x × y)/r i ) × r i ) (1) where W, H represent the width and height of an anchor; r i = (r 1 , r 2 , r 3 ) = (0.5, 1, 2), and r i indicates the aspect ratio; s j = (s 1 , s 2 , s 3 ) = (3,4,5), and 2 s j indicates the expansion factor of size; round stands for rounding; and x, y indicate the width and height of pixels of a feature point mapped back to the original input image, and x = y = 16.…”

Section: Working Principle Of Rpnmentioning

confidence: 99%

“…The development of an autonomous vehicle system that enhances safety and traffic efficiency is in progress continuously; this system requires a road environment detection facility in order to control the host vehicle [1,2]. The detection of preceding vehicles plays a decisive role in realizing rational planning of the driving path, maintaining the correct distance, and ensuring driving safety for autonomous vehicles [3,4]. Recently, preceding vehicle detection has become a research hotspot due to its necessity for autonomous vehicles, and many detection algorithms have been proposed [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient

Cui

Wang

et al. 2019

Electronics

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At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host vehicle speed increases or there is an occlusion in front, the performance of the Faster R-CNN algorithm usually degrades. To obtain better performance on preceding vehicle detection when the speed of the host vehicle changes, a speed classification random anchor (SCRA) method is proposed. The reasons for degraded detection accuracy when the host vehicle speed increases are analyzed, and the factor of vehicle speed is introduced to redesign the anchors. Redesigned anchors can adapt to changes of the preceding vehicle size rule when the host vehicle speed increases. Furthermore, to achieve better performance on occluded vehicles, a Q-square penalty coefficient (Q-SPC) method is proposed to optimize the Faster R-CNN algorithm. The experimental validation results show that compared with the Faster R-CNN algorithm, the SCRA and Q-SPC methods have certain significance for improving preceding vehicle detection accuracy.

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“…Although there are multiple alternatives within the domain of EA, this work employs the genetic algorithm (GA) [6] since it can be applied to our problem in quite a straightforward manner. GA works with a population of individuals (chromosomes) which represent possible solutions to the problem.…”

Section: B Determination Of the Cross-terms Part Using Eamentioning

confidence: 99%

Incorporation of Cross-Terms in Memory Polynomial Using Evolutionary Algorithm for Accurate Behavioral Modeling of RF Power Amplifiers

Yuan

Feng

Zhou³

2014

IEEE Microw. Wireless Compon. Lett.

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This letter proposes a novel behavioral modeling approach for power amplifiers (PAs) which incorporates the most effective cross-terms in memory polynomial (MPM) by using an ad hoc evolutionary algorithm (EA) in conjunction with an effective parameter identification method. This new proposal takes advantage of the relative simplicity and high efficiency of MPM for PA behavioral modeling, whereas incorporation of the cross-terms further enhances modeling performance. Validation and comparison results on a class-AB high PA with the Mobile Multimedia Broadcasting (MMB) signal demonstrate the superior performance of the proposed model. Index Terms-Behavioral model, evolutionary algorithm (EA), memory polynomial (MPM), nonlinear distortions, power amplifier (PA).

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A Fast Evolutionary Algorithm for Real-Time Vehicle Detection

Cited by 35 publications

References 38 publications

Local Density Encoding for Robust Stereo Matching

Local Density Encoding for Robust Stereo Matching

Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient

Incorporation of Cross-Terms in Memory Polynomial Using Evolutionary Algorithm for Accurate Behavioral Modeling of RF Power Amplifiers

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