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Vehicle Occurrence-based Parking Space Detection (2306.09940v1)
Published 16 Jun 2023 in cs.CV, cs.AI, and cs.LG
Abstract: Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. Computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled, impacting the cost and feasibility of deployment. To fill this gap, this work presents an automatic parking space detection method, which receives a sequence of images of a parking lot and returns a list of coordinates identifying the detected parking spaces. The proposed method employs instance segmentation to identify cars and, using vehicle occurrence, generate a heat map of parking spaces. The results using twelve different subsets from the PKLot and CNRPark-EXT parking lot datasets show that the method achieved an AP25 score up to 95.60\% and AP50 score up to 79.90\%.
- P. R. Almeida, L. S. Oliveira, A. S. Britto Jr, E. J. Silva Jr, and A. L. Koerich, “Pklot–a robust dataset for parking lot classification,” Expert Systems with Applications, vol. 42, no. 11, pp. 4937–4949, 2015.
- G. Amato, F. Carrara, F. Falchi, C. Gennaro, and C. Vairo, “Car parking occupancy detection using smart camera networks and deep learning,” in 2016 IEEE Symposium on Computers and Communication (ISCC). IEEE, 2016, pp. 1212–1217.
- R. M. Nieto, Á. García-Martín, A. G. Hauptmann, and J. M. Martínez, “Automatic vacant parking places management system using multicamera vehicle detection,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 1069–1080, 2018.
- C. Biyik, Z. Allam, G. Pieri, D. Moroni, M. O’Fraifer, E. O’Connell, S. Olariu, and M. Khalid, “Smart parking systems: Reviewing the literature, architecture and ways forward,” Smart Cities, vol. 4, no. 2, pp. 623–642, 2021.
- P. Almeida, J. Alves, R. Parpinelli, and J. Barddal, “A systematic review on computer vision-based parking lot management applied on public datasets,” Expert Systems With Applications, vol. 198, p. 116731, 2022.
- R. Bohush, P. Yarashevich, S. Ablameyko, and T. Kalganova, “Extraction of image parking spaces in intelligent video surveillance systems,” Machine Graphics and Vision, vol. 27, no. 1-4, pp. 47–62, 2018.
- W. Zhang, J. Yan, and C. Yu, “Smart parking system based on convolutional neural network models,” in 6th Int. Conf. on Information Science and Control Engineering, 2019, pp. 561–566.
- S. Vítek and P. Melničuk, “A distributed wireless camera system for the management of parking spaces,” Sensors, vol. 18, no. 1, p. 69, 2018.
- X. Li and S. Chuah, M. C. andBhattacharya, “UAV assisted smart parking solution,” in International Conference on Unmanned Aircraft Systems, 2017, pp. 1006–1012.
- H. Padmasiri, R. Madurawe, C. Abeysinghe, and D. Meedeniya, “Automated vehicle parking occupancy detection in real-time,” in Moratuwa Engineering Research Conference, 2020, pp. 1–6.
- P. R. Kirtibhai, , and P. Meduri, “Faster r-cnn based automatic parking space detection,” in International conference on machine learning and machine intelligence, 202, pp. 105–109.
- C. Zhang and B. Du, “Image-based approach for parking-spot detection with occlusion handling,” Journal of Transportation Engineering, Part A: Systems, vol. 146, no. 9, p. 04020098, 2020.
- T. Agrawal and S. Urolagin, “Multi-angle parking detection system using mask r-cnn,” in Int. Conf. on Big Data Engineering and Technology, vol. 2020, 2020, pp. 76–80.
- A. Coleiro, D. Scerri, and I. Briffa, “Car parking detection in a typical village core street using public camera feeds,” in 10th Int. Conf. on Consumer Electronics, 2020, pp. 1–6.
- K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV. IEEE, 2017, pp. 2961–2969.
- K. Pannerselvam, “Adaptive parking slot occupancy detection using vision transformer and llie,” in Int. Smart Cities Conference, 2021.
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR, 2016, pp. 770–778.
- S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in NIPS, 2015, pp. 91–99.
- N. Hurst-Tarrab, L. Chang, M. Gonzalez-Mendoza, and N. Hernandez-Gress, “Robust parking block segmentation from a surveillance camera perspective,” Applied Sciences, vol. 10, no. 15, p. 5364, 2020.
- R. Patel and P. Meduri, “Car detection based algorithm for automatic parking space detection,” in 19th IEEE Int. Conf. on Machine Learning and Applications, 2020, pp. 1418–1423.
- M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International journal of computer vision, vol. 88, no. 2, pp. 303–338, 2010.
- Z. Cai and N. Vasconcelos, “Cascade r-cnn: Delving into high quality object detection,” in CVPR, 2018, pp. 6154–6162.
- Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A convnet for the 2020s,” in CVPR, 2022, pp. 11 976–11 986.
- G. Amato, F. Carrara, F. Falchi, C. Gennaro, C. Meghini, and C. Vairo, “Deep learning for decentralized parking lot occupancy detection,” Expert Systems with Applications, vol. 72, pp. 327–334, 2017.
- I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101, 2017.
- ——, “Sgdr: Stochastic gradient descent with warm restarts,” arXiv preprint arXiv:1608.03983, 2016.
- S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Computer vision, graphics, and image processing, vol. 39, no. 3, pp. 355–368, 1987.