Abstract
The existing pedestrian detection suffers the low accuracy when the environment changes dramatically. In order to solve the problem, a pedestrian detection model combining information fusion and multi-scale detection is proposed. The model is composed of a retinex algorithm and an improved YOLOv3 algorithm. Retinex algorithm is selected as the preprocessing algorithm to improve the brightness and contrast of pedestrians. The model improves the YOLOv3 algorithm by adding multiple scale detections. The K-means is used to determine the number of optimal anchors and the aspect ratio. By testing on the standard data set, the mean average precision (mAP) of the joint detection model increases from the original 80.69–91.07%, and the recall increases from 65.22 to 87.48%. The comparative experiments show that the improved model performs good robustness and generalization ability on the problem of low pedestrian detection accuracy in complex environments.
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Suprit, D.B., Abhijeet, V.N.: Crowd anomaly detection and localization using histogram of magnitude and momentum. Vis. Comput. 36(3), 609–620 (2020)
Lin Feng, Y.L., Zan, L., Meng, Z., Feilong, W., Shenglan, L.: Discriminative bit selection hashing in rgb-d based object recognition for robot vision. Assembly Automation (2019)
Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C., Winner, H.: Three decades of driver assistance systems: review and future perspectives. IEEE Intell. Transp. Syst. Magaz. 6(4), 6–22 (2014)
Pang, Y., Yuan, Y., Li, X., Pan, J.: Efficient hog human detection. Signal Process. 91(4), 773–781 (2011)
Chengbin, Z., Huadong, M., Anlong, M.: Fast human detection using mi-svm and a cascade of hog-lbp features. In: 2010 IEEE International Conference on Image Processing, pp. 3845–3848. IEEE (2010)
Navneet Dalal, B.T.: Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893. IEEE (2005)
Meyer, D., Leisch, F., Hornik, K.: The support vector machine under test. Neurocomputing 55(1–2), 169–186 (2003)
Zhong-Qiu, Z., Haiman, B., Donghui, H., Wenjuan, C., Hervé, G.: Pedestrian detection based on fast r-cnn and batch normalization. In: International Conference on Intelligent Computing, pp. 735–746. Springer (2017)
Shaoqing, R., Kaiming, H., Ross, G., Jian, S.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Yeong-Hyeon, B., Keun-Chang, K.: A performance comparison of pedestrian detection using faster rcnn and acf. In: 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 858–863. IEEE (2017)
Kaiming, H., Georgia, G., Piotr Dollár, R.G.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Joseph, R., Santosh, D., Ross, G., Ali, F.: You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Wei, L., Dragomir, A., Dumitru, E., Christian, S., Scott, R., Cheng-Yang, F., Alexander, C.B.: Ssd: Single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer (2016)
Alexander, W., Mohammad, J.S., Francis, L., Brendan, C.: Tiny ssd: A tiny single-shot detection deep convolutional neural network for real-time embedded object detection. In: 2018 15th Conference on Computer and Robot Vision (CRV), pp. 95–101. IEEE (2018)
Chong, L., Rong, W., Jinze, L., Linyu, F.: Face detection based on yolov3. In: Recent Trends in Intelligent Computing, Communication and Devices, pp. 277–284. Springer (2020)
Ajit, J., Prerana, M., Vinay, K., Brejesh, L.: Aerial multi-object tracking by detection using deep association networks. In: 2020 National Conference on Communications (NCC), pp. 1–6. IEEE (2020)
Tsung-Yi, L., Michael, M., Serge, B., James, H., Pietro, P., Deva, R., Piotr, D., Lawrence, Z.: Microsoft coco: Common objects in context. In European conference on computer vision, pp. 740–755. Springer (2014)
Zia-ur, R., Daniel, J.J., Glenn, A.W.: Retinex processing for automatic image enhancement. J. Electron. Imag. 13(1), 100–111 (2004)
Zietkiewicz, E., Rafalski, A., Labuda, D.: Genome fingerprinting by simple sequence repeat (ssr)-anchored polymerase chain reaction amplification. Genomics 20(2), 176–183 (1994)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)
Qin, Y., Luo, F., Li, M.: A medical image enhancement method based on improved multi-scale retinex algorithm. J. Med. Imag. Health Inf. 10(1), 152–157 (2020)
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., Liang, Z.: Apple detection during different growth stages in orchards using the improved yolo-v3 model. Comput. Electron. Agric. 157, 417–426 (2019)
Mujtaba, A., Jie, Y., Jiang, H., Pourya, S., Xiangjian, H.: Multi-frame feature-fusion-based model for violence detection. Vis. Comput. pp. 1–17 (2020)
Tsung-Yi, L., Piotr Dollár, R.G., Kaiming, H., Bharath, H., Serge, B.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
John, A.H., Manchek, A.W.: Algorithm as 136: A k-means clustering algorithm. J. Royal Stat. Soc. Ser. c 28(1), 100–108 (1979)
Zitnick, C.L., Piotr, D.: Edge boxes: Locating object proposals from edges. In: European Conference on Computer Vision, pp. 391–405. Springer (2014)
Danielsson, P.-E.: Euclidean distance mapping. Computer Graph. Image Process. 14(3), 227–248 (1980)
Vishnu, G.N., Guruprasad, K.R.: Multi-robot coverage using voronoi partitioning based on geodesic distance. In: Control Instrumentation Systems, pp. 59–66. Springer (2020)
Acknowledgements
This work was supported by Project supported by National Natural Science Foundation of China (No. 62003296), the Natural Science Foundation of Hebei (No. F2020203031), Science and Technology Project of Hebei Education Department (No. QN2020225)
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Zhang, H., Hu, Z. & Hao, R. Joint information fusion and multi-scale network model for pedestrian detection. Vis Comput 37, 2433–2442 (2021). https://doi.org/10.1007/s00371-020-01997-0
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DOI: https://doi.org/10.1007/s00371-020-01997-0