Detection of partially occluded pedestrians by an enhanced cascade detector

Li, Wenhui; Ni, Hongyin; Wang, Ying; Fu, Bo; Liu, Peixun; Wang, Shoujia

doi:10.1049/iet-its.2012.0173

Cited by 9 publications

(14 citation statements)

References 29 publications

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“…Zhu et al [29] introduced an efficient pedestrian detection method based on HOG and AdaBoost. In our previous work, we use HOG feature to detect pedestrian [14]. The HOG feature is represented by calculating the histogram of oriented gradient of local region in the image.…”

Section: Hog and Adaboost Classifier Feature Similarity Measurementioning

confidence: 99%

“…Although the voting method can reduce the false alarm rate, the detection rate is reduced either. Processing time is another indicator to measure the performance of [11] 98%/0% 510 Li et al [14] 98%/1% 500 Ali and Shah [15] 90.2%/0.6% 500 Taillight algorithms; Ali and Wang's methods outperform our method in terms of accuracy, but the processing time of their methods is above 500 ms. Considering both the accuracy and the processing time of algorithms, our method outperforms the other methods.…”

Section: Experiments 3 (Algorithm Comparison)mentioning

confidence: 99%

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Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

Li¹,

Liu²,

Wang³

et al. 2014

Journal of Applied Mathematics

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Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS) and Blind Spot Detection Systems (BSDS). The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS).

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Section: Hog and Adaboost Classifier Feature Similarity Measurementioning

confidence: 99%

Section: Experiments 3 (Algorithm Comparison)mentioning

confidence: 99%

Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

Li¹,

Liu²,

Wang³

et al. 2014

Journal of Applied Mathematics

Self Cite

View full text Add to dashboard Cite

show abstract

“…To protect them and to reduce other potential risks, pedestrian detection and classification systems are widely employed. Many pedestrian classifiers: holistic classifier [5][6][7][8][9][10][11], part-based classifier [12][13][14][15][16][17][18][19][20], and deep model classifier [19,21,22], have already been proposed and are actually in use, but several challenges remain to be solved, such as illumination changes, occlusion, variation of pose and shapes, variation of appearances, and inconsistency of surroundings. Besides, occlusion handling under complex backgrounds in the real-world environment may involve further difficulties [8,14,[19][20][21]23].…”

Section: Introductionmentioning

confidence: 99%

“…In the classification method, the well-known techniques such as support vector machine (linear and latent) [4,5,10,12,25,38], AdaBoost [7,9,34,39], neural network [2,3], random forest [3], and cascade [7,17,27] had been widely used. Among them, the linear SVM is one of the useful techniques that can compute faster than a latent SVM in terms of performance and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

Thu

Suvonvorn

2020

Advances in Multimedia

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Pedestrian detection and classification are of increased interest in the intelligent transportation system (ITS), and among the challenging issues, we can find limitations of tiny and occluded appearances, large variation of human pose, cluttered background, and complex environment. In fact, a partial occlusion handling is important in the case of detecting pedestrians, in order to avoid accidents between pedestrians and vehicles, since it is difficult to detect when pedestrians are suddenly crossing the road. To solve the partial occlusion problem, a pyramidal part-based model (PPM) is proposed to obtain a more accurate prediction based on the majority vote of the confidence score of the visible parts by cascading the pyramidal structure. The experimental results on the proposed scheme achieved 96.25% accuracy on the INRIA dataset and 81% accuracy on the PSU (Prince of Songkla University) dataset. Our proposed model can be applied in the real-world environment to classify the occluded part of pedestrians with the various information of part representation at each pyramid layer.

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“…In [25][26], AdaBoost classifier is used which improves the detection accuracy by 10 %. AdaBoost is preferred for object detection employing cascade classification and various researchers [27] employed this approach. In summary, HOG comes out to be better detector than others for pedestrian detection and researchers are combining it with other techniques under different scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Robust mRMR Based Pedestrian Detection Approach Using Shape Descriptor

Kumar¹,

Mishra²

2019

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Pedestrian detection is a fundamental problem in various computer vision applications and is addressed by complex solutions involving complex feature extractor and classification techniques. In this paper, feature selector is used along with Histogram of Significant Gradients (HSG) descriptor and linear SVM classifier to enhance the detection accuracy and reduce the processing time. A feature selector organizes the extracted features in decreasing order of their significance. A comparative study has been done employing different classifiers and feature selectors and a system is proposed with better detection capability. The proposed scheme is found to have a 50 % reduction in feature size, 67 % reduction in processing time and 3.93 % improvement in accuracy.

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Detection of partially occluded pedestrians by an enhanced cascade detector

Cited by 9 publications

References 29 publications

Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

A Robust mRMR Based Pedestrian Detection Approach Using Shape Descriptor

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