Abstract
To solve the challenges in traffic object identification, fuzzification, and simplification in a real traffic environment, it is highly required to develop an automatic detection and classification technique for roads, automobiles, and pedestrians with multiple traffic objects inside the same framework. The proposed method has been evaluated on a database with complicated poses, motions, backgrounds, and lighting conditions for an urban scenario where pedestrians are not obstructed. The suggested CNN classifier has an FPR of less than that of the SVM classifier. Confirming the significance of automatically optimized features, the SVM classifier's accuracy is equal to that of the CNN. The proposed framework is integrated with the additional adaptive segmentation method to identify pedestrians more precisely than the conventional techniques. Additionally, the proposed lightweight feature mapping leads to faster calculation times and it has also been verified and tabulated in the results and discussion section.
References
Balasubramaniam, Vivekanadam. "Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis." Journal of Artificial Intelligence and Capsule Networks 3, no. 1: 34-42.
D. M. Gavrila and J. Giebel, “Shape-based pedestrian detection and tracking,” in Proc. IEEE Intelligent Vehicle Symposium, IV 2002, Versailles, France, June 2002.
Adam, Edriss Eisa Babikir. "Evaluation of Fingerprint Liveness Detection by Machine Learning Approach-A Systematic View." Journal of ISMAC 3, no. 01 (2021): 16-30.
Manoharan, J. Samuel. "Capsule Network Algorithm for Performance Optimization of Text Classification." Journal of Soft Computing Paradigm (JSCP) 3, no. 01 (2021): 1-9.
K. Levi and Y. Weiss, “Learning object detection from a small number of examples: the importance of good features,” in Proc. International Conference on Computer Vision ICCV 2003, Nice, France, Oct. 2003.
Adam, Edriss Eisa Babikir. "Survey on Medical Imaging of Electrical Impedance Tomography (EIT) by Variable Current Pattern Methods." Journal of ISMAC 3, no. 02 (2021): 82-95.
J. Louie, “A biological model of object recognition with feature learning,” Master’s thesis, Massachusetts Institute of Technology, Cambridge, 2003.
Karuppusamy, P. "Building Detection using Two-Layered Novel Convolutional Neural Networks." Journal of Soft Computing Paradigm (JSCP) 3, no. 01 (2021): 29-37.
H. Schneiderman, “Learning a restricted bayesian network for object detection,” in Proc. Computer Vision and Pattern Recognition CVPR’04, Washington, DC, USA, June 2004.
Vijayakumar, T., Mr R. Vinothkanna, and M. Duraipandian. "Fusion based Feature Extraction Analysis of ECG Signal Interpretation–A Systematic Approach." Journal of Artificial Intelligence 3, no. 01 (2021): 1-16.
D. M. Gavrila and S. Munder, “Vision-based pedestrian protection: The PROTECTOR system,” in Proc. IEEE Intelligent Vehicle Symposium, IV 2004, Parma, Italy, June 2004.
Chen, Joy Iong-Zong. "Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach." Journal of ISMAC 3, no. 02 (2021): 132-148.
Sharma, Rajesh, and Akey Sungheetha. "An Efficient Dimension Reduction based Fusion of CNN and SVM Model for Detection of Abnormal Incident in Video Surveillance." Journal of Soft Computing Paradigm (JSCP) 3, no. 02 (2021): 55-69.
M. Soga, T. Kato, M. Ohta, and Y. Ninomiya, “Pedestrian detection using stereo vision and tracking,” in Proc. World Congress on ITS, Nagoya, Japan, Oct. 2004.
Sungheetha, Akey, and Rajesh Sharma. "Design an Early Detection and Classification for Diabetic Retinopathy by Deep Feature Extraction based Convolution Neural Network." Journal of Trends in Computer Science and Smart technology (TCSST) 3, no. 02 (2021): 81-94.
Smys, S., and Wang Haoxiang. "Naïve Bayes and Entropy based Analysis and Classification of Humans and Chat Bots." Journal of ISMAC 3, no. 01 (2021): 40-49.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: convolutional networks for biomedical image segmentation. arXiv:1505.04597.
Badrinarayanan, V., Kendall, A., and Cipolla, R. (2015). Segnet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561.
Long, J., Shelhamer, E., and Darrell, T. (2015). “Fully convolutional networks for semantic segmentation,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Boston, MA).
Liu, T. R., and Chan, S. C. (2015). “A hierarchical semantic image labelling method via randomforests,” in TENCON 2015-2015 IEEE Region 10 Conference (Macao), 1–5.
Ciresan, D., Giusti, A., Gambardella, L. M., and Schmidhuber, J. (2012). “Deep neural networks segment neuronal membranes in electron microscopy images,” in Advances in Neural Information Processing Systems (Lake Tahoe), 2843–2851.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Las Vegas, NV), 770–778.
Dayana, A. Mary, and WR Sam Emmanuel. "A Patch-Based Analysis for Retinal Lesion Segmentation with Deep Neural Networks." In International conference on Computer Networks, Big data and IoT, pp. 677-685. Springer, Cham, 2019.
Huang, J.-J., and Siu, W.-C. (2017). Learning hierarchical decision trees for single image super-resolution. IEEE Trans. Circ. Syst. Video Technol. 27, 937–950. doi: 10.1109/TCSVT.2015.2513661
Hussain, J. "A Shape-Based Character Segmentation Using Artificial Neural Network for Mizo Script." In International Conference on Communication, Computing and Electronics Systems, pp. 231-239. Springer, Singapore, 2020.
Huang, J. J., Siu, W. C., and Liu, T. R. (2015). Fast image interpolation via random forests. IEEE Trans. Image Process. 24, 3232–3245. doi: 10.1109/TIP.2015.2440751
Swetha, O., and C. Ramachandran. "Counting and Tracking of Vehicles and Pedestrians in Real Time Using You Only Look Once V3." In Data Intelligence and Cognitive Informatics, pp. 873-886. Springer, Singapore, 2021.
Kapuriya, B. R., Debasish Pradhan, and Reena Sharma. "Selective segmentation of piecewise homogeneous regions." In International Conference on Innovative Data Communication Technologies and Application, pp. 535-542. Springer, Cham, 2019.
Mittal, Neetu, and Alexander Gelbukh. "Change detection in remote-sensed data by particle swarm optimized edge detection image segmentation technique." In Innovative Data Communication Technologies and Application, pp. 809-817. Springer, Singapore, 2021.