An Analysis of Lightweight Convolutional Neural Networks for Parking Space Occupancy Detection
Commercial parking space occupancy detection systems used to be mostly sensor-based. Very recently, we have seen great success in computer vision techniques which allow us to utilize the CCTV camera feed in real-time. In this paper, we review multiple existing convolutional neural network models of various sizes to analyze the benefits of using each model for parking space occupancy detection. We measure the accuracy, required floating point operations, and parameter counts of each model. We then compare model performance over different conditions such as camera perspectives, weather types, different parking lots, and lighting conditions. Based on our observations and experience, we introduce three novel architectures, two based on the DenseNet architecture - Mini DenseNet and Simple DenseNet, and CoarseNet - a multi-layer perceptron. We compare our proposed models to other models of various sizes. Performance results show that these three models have advantages over existing models in parameter counts, accuracy, and resilience to new camera perspectives.
Image Classification, Deep Learning, Neural Networks, Parking Space, Vehicle Detection
Ellis, Joshua D., Anthony Harris, Naseem Saquer, and Razib Iqbal. "An Analysis of Lightweight Convolutional Neural Networks for Parking Space Occupancy Detection." In 2021 IEEE International Symposium on Multimedia (ISM), pp. 261-268. IEEE, 2021.
IEEE International Symposium on Multimedia