Abstract
Vehicle detection and tracking plays a major role in military and civilian applications. The accurate detection of multiple vehicles in a complicated traffic environment is too difficult. This process is made more difficult, if there are occlusions between vehicles. The optimal feature selection of a vehicle is also a challenging task. These issues are focused in the existing work namely convolution neural network (CNN) based vehicle detection. However in this work, feature selection is not concentrated and with the lesser feature information classification accuracy is reduced considerably. This is focused and resolved in the proposed research by introducing the method namely Enhanced Convolution neural network with Support Vector Machine (ECNN-SVM) based vehicle detection. In this work initially feature extraction is done. Haar like features are used for this purpose. This has compact representation, capture information from multiple scales, encoded edge and structural information and can be efficient computation. Haar like feature pool is large scale in nature. Enhanced bat optimization is used to select features from this pool. This selection is done by combining sample’s class label and feature value. Confidence level between two thresholds of tracking rectangles is used for classification. Support vector machine (SVM) classifier is combined with local binary pattern to perform this classification. The interference areas between moving objects and vehicles are removed by Enhanced Convolutional Neural Network (ECNN) classifier. The efficiency of the proposed method is computed by using experimental and theoretical analysis.
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07 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04094-3
References
Barnich O, Van Droogenbroeck M (2010) ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724
Buczak AL, Guven E (2015) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176
Cai Y, Sun X, Wang H, Chen L, Jiang H (2016) Night-time vehicle detection algorithm based on visual saliency and deep learning. J Sens 2016:8046529. https://doi.org/10.1155/2016/8046529
Chen L, Ye F, Ruan Y, Fan H, Chen Q (2018) An algorithm for highway vehicle detection based on convolutional neural network. EURASIP J Image Video Process 1:1–7
Dallalzadeh E, Guru DS (2010) Feature-based tracking approach for detection of moving vehicle in traffic videos. In: ACM proceedings of the first international conference on intelligent interactive technologies and multimedia, pp 254–260
De Morsier F, Tuia D, Borgeaud M, Gass V, Thiran JP (2013) Semi-supervised novelty detection using svm entire solution path. IEEE Trans Geosci Remote Sens 51(4):1939–1950
Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2009) Object detection with discriminatively trained part-based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645
Gaikwad VM, Sonkamble B (2015) Analysis and classification of moving vehicles using KNN and decision tree. Int J Adv Res Comput Sci 6(2):152–156
Huang Y, Essa I (2005) Tracking multiple objects through occlusions. In: IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 2, pp 1051–1058
Huang CL, Liao WC (2004) A vision-based vehicle identification system. In: IEEE proceedings of the 17th international conference on pattern recognition, vol 4, pp 364–367
Khan K, Nikov A, Sahai A (2011) A fuzzy bat clustering method for ergonomic screening of office workplaces. In: Third international conference on software, services and semantic technologies, pp 59–66
Kim H (2019) Multiple vehicle tracking and classification system with a convolutional neural network. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01429-5
Lin CT, Hsu SC, Lee JF, Yang CT (2013) Boosted vehicle detection using local and global features. J Signal Inf Process 4(3):243–252
Liu Y, Wang Y, Sowmya A, Chen F (2016) Soft Hough forest-ERTs: generalized Hough transform based object detection from soft-labelled training data. Pattern Recogn 60:145–156
Min W, Fan M, Guo X, Han Q (2018) A new approach to track multiple vehicles with the combination of robust detection and two classifiers. IEEE Trans Intell Transp Syst 19(1):174–186
Qian Z, Shi H, Yang J, Duan L (2013) Video-based multiclass vehicle detection and tracking. Int J Comput Sci Issues (IJCSI) 10(1):570–578
Qu SR, Li J, Shu Y (2019) Accurate vehicle location and tracking algorithms based on improved kernelized correlation motion model and Kalman filter in intelligent transport surveillance system. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01589-4
Satzoda RK, Trivedi MM (2015) Multipart vehicle detection using symmetry-derived analysis and active learning. IEEE Trans Intell Transp Syst 17(4):926–937
Sun ZH, Bebi G, Miller R (2006) On-road vehicle detection: a review. IEEE Trans Pattern Anal Mach Intell 28(5):694–711
Trivedi MM, Gandhi T, McCall J (2007) Looking-in and looking-out of a vehicle: computer-vision-based enhanced vehicle safety. IEEE Trans Intell Transp Syst 8(1):108–120
Velazquez-Pupo R, Sierra-Romero A, Torres-Roman D, Shkvarko YV, Santiago-Paz J, Gómez-Gutiérrez D, Robles-Valdez D, Hermosillo-Reynoso F, Romero-Delgado M (2018) Vehicle detection with occlusion handling, tracking, and OC-SVM classification: a high performance vision-based system. Sensors 18(374):2–23
Wang B, Qi Z, Chen S, Liu Z, Ma G (2017) Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system. PLoS One 12(3):1–20
Wen X, Shao L, Fang W, Xue Y (2015) Efficient Feature selection and classification for vehicle detection. IEEE Trans Circuits Sys Video Technol 25(3):508–517
Xiao Y, Keung J, Mi Q, Bennin KE (2017) Improving bug localization with an enhanced convolutional neural network. In: Asia-Pacific software engineering conference (APSEC), pp 338–347
Yang B, Chen S (2013) A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 120(23):365–379
Yu S, Wu Y, Li W, Song Z, Zeng W (2017) A model for fine-grained vehicle classification based on deep learning. NeuroComputing 257:97–103
Yuan XC, Wu LS, Peng Q (2015) An improved Otsu method using the weighted object variance for defect detection. Appl Surf Sci 349:472–484
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04094-3
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Ranjeeth Kumar, C., Anuradha, R. RETRACTED ARTICLE: Feature selection and classification methods for vehicle tracking and detection. J Ambient Intell Human Comput 12, 4269–4279 (2021). https://doi.org/10.1007/s12652-020-01824-3
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DOI: https://doi.org/10.1007/s12652-020-01824-3