Pedestrian Detection in Haze Environments Using Dark Channel Prior and Histogram of Oriented Gradient

Ding, Baoyang; Liu, Zhenghua; Sun, Yang

doi:10.1109/imccc.2018.00211

Cited by 4 publications

(6 citation statements)

References 29 publications

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“…In addition, a good amount of attention was given to developing safe AVS systems for pedestrian detection. Multiple deep learning approaches such as DNN, CNN, YOLO V3-Tiny, DeepSort R-CNN, single-shot late-fusion CNN, Faster R-CNN, R-CNN combined ACF model, dark channel prior-based SVM, attention-guided encoder-decoder CNN outperformed the baseline of applied datasets that presented a faster warning area by bounding each pedestrian in real time [61], detection in crowded environments, and dim lighting or haze scenarios [62,72] for position estimation [72], minimizing computational cost and outperforming state-of-the-art methods [120]. The approaches offer an ideal pedestrian method once their technical challenges have been overcome, for example, dependency on preliminary boxing during detection, presumption of constant depths in input image and improvement to avoid missing rate when dealing with a complex environment.…”

Section: Discussionmentioning

confidence: 99%

“…Vehicle Detection [34][35][36][37][38][39][40][41][42][43][44][45] Traffic Sign and Light Recognition [46][47][48][49][50][51][52][53][54][55][56][57][58][59] Pedestrian Detection [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78] Lane Detection and Tracking [44, Traffic Scene Analysis [55,[102][103][104][105][10...…”

Section: Perceptionmentioning

confidence: 99%

“…Correspondingly, de-hazing algorithms were one of the solutions to fix vision problems which can be implemented in detection of pedestrians in haze conditions. For instance, a related approach for pedestrian detection in haze conditions was proposed by Ding et al, implementing a synthesized haze version of the INRIA dataset using dark channel prior-based linear SVM and HOG algorithm [72]. Nonetheless, the approach received poor recall value, scoring 67.88% in predicting constant depths of input images.…”

Section: Pedestrian Detectionmentioning

confidence: 99%

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Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

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In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based HUD, analyzing research works from 2011 to 2021 that focus on RGB camera vision. The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions. The contribution of the literature review includes detailed analysis of current state-of-the-art deep learning methods that only rely on RGB camera vision rather than complex sensor fusion. It is expected to offer a pathway for the rapid development of cost-efficient and more secure practical autonomous vehicle systems.

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Section: Discussionmentioning

confidence: 99%

Section: Perceptionmentioning

confidence: 99%

Section: Pedestrian Detectionmentioning

confidence: 99%

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Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

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“…Gang [32] et al incorporated low-rank sparse and adaptive dictionary learning template with the classical particle filter algorithm to solve the difficulty of vehicle tracking in haze scenes. Another solution to cope with severe weather is to perform thermal infrared tracking [33], such as MCFTS [34], MLSSNet [35], MMNet [36] proposed by Liu et al In some object detection tasks [37]- [40], researchers proposed a series of pedestrian and vehicle detection methods for the haze environment. Our DH-SiamRPN provides a new fusion algorithm to solve the object tracking problem in this scenario.…”

Section: B Computer Vision In Challenging Environmentsmentioning

confidence: 99%

An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

et al. 2021

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The haze scenes will bring negative impact to the object tracking process. In haze scenes, aerosols in the air will decrease the information entropy of the image generated by the imaging device. The reduction of information entropy means the loss of image detail information, which will mislead the existing algorithms to extract wrong object feature, causing the failure of tracking. Firstly, this paper explores the problem and proposes an end-to-end Dehazing Siamese Region Proposal Network (DH-SiamRPN). Then, we innovatively design an adaptive transmittance estimation module based on image quality evaluation index, and pre-integrate it into the dark channel dehazing algorithm to preprocess the video sequence. After that, based on the siamese network framework, AlexNet is employed to extract the depth features of the image pair, and the cross-correlation operation of classification and regression is performed according to the regional proposal network (RPN). In addition, inspired by DIoU modeling ideas, we innovatively optimize the proposal selection part. The experimental results demonstrate that the DH-SiamRPN has obvious advantages in precision and speed compared with other state-of-the-art trackers. Its tracking performance meets the requirements of high robustness and speed.

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“…Traditional pedestrian detection methods are mostly implemented step by step based on statistical learning. First, effective feature extraction is conducted in the candidate region of the detection image, and then input to the classifier for discrimination, and finally output the results combined with the detection model [16][17][18]. Dollar et al [19] proposed a research method for multi-scale pedestrian detection using fast feature pyramids based on aggregated channel features (ACF).…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Cao

Song

Peng

et al. 2020

Sensors

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Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

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Pedestrian Detection in Haze Environments Using Dark Channel Prior and Histogram of Oriented Gradient

Cited by 4 publications

References 29 publications

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

An End-to-End Dehazing Siamese Region Proposal Network for High Robustness Object Tracking

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

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