Smart parking system plays a critical role in the overall development of the cities. The capability to precisely detect an open parking space nearby is necessary for autonomous vehicle parking for smart cities. Finding parking spaces is a big issue in big cities. Many of the existing parking guidance systems use fixed IoT sensors or cameras that are unable to offer information from the perspective of the driver. Accurately locating parking spaces can be difficult since they come in a range of sizes and colors that are blocked by objects that seem different depending on the environmental lighting. There are numerous auto industry players engaged in the advanced testing of driverless cars. A vacant parking space must be found, and the car must be directed to park there in order for the operation to succeed. The machine learning-based algorithms created to locate parking spaces and techniques and methods utilizing dashcams and fish-eye cameras are reviewed in this study. In response to the increase in dashcams, neural network-based techniques are created for identifying open parking spaces in dashcam videos. The paper proposed the review of the existing parking slot types and their detection techniques. The review will highlight the importance and scope of a smart parking system for smart cities. 

A Review of Parking Slot Types and their Detection Techniques for Smart Cities

In this paper, a special design system is developed based on the design of the packaging bottle to achieve the effective acquisition of the image of the cross-section of the packaging bottle to be measured under the condition of limited space size, avoiding the distortion of the object to be measured. At the same time, the image of the area where the target packaging bottle is located is segmented, and the curve features are quickly determined and effectively matched with the template library to realize the recognition of the shape features of the bottle. In this paper, the design of the packaging bottle is first designed by mechanism design and 3D modeling, followed by rapid prototyping methods such as 3D printing, and the prototype is made for functional verification. Finally, the transmission speed and stability of the design system for packaging bottle recognition are improved through structural analysis and optimization methods. To realize the intelligent control of the packaging bottle transmission and identification system, the hardware control circuit is designed and the relevant intelligent control program is prepared based on the embedded system so that the packaging bottles in the transmission process can be quickly and accurately positioned and identified. An improved AdaBoost algorithm is proposed for packaging bottle detection. In the process of algorithm learning, the Haar features are too large and time-consuming, and the training sample is cropped to remove the sample edge pixels, which effectively reduces the number of features, thus reducing the computation. The proposed optical flow method is used to obtain the motion region in the video image as the region of interest, and the canny operator is used in the region of interest for edge detection, and the region of interest is filtered by the edge energy to exclude the noninterest region. Finally, the AdaBoost algorithm is used to detect the region of interest, which reduces the detection area and decreases the detection time. The improved AdaBoost algorithm has a high accuracy improvement over the traditional AdaBoost algorithm for the recognition of various packaging bottles with relatively suitable training set samples, and the system recognition time has reached the requirements of industrial recognition.

/pdf/a-study-on-the-design-and-implementation-of-an-improved-12tvycij.pdf

A Study on the Design and Implementation of an Improved AdaBoost Optimization Mathematical Algorithm Based on Recognition of Packaging Bottles

A multitarget real-time tracking system for panoramic video with multisensor information fusion dual neural networks is studied and implemented by combining dual neural networks, fused geometric features, and deep learning-based target real-time tracking algorithms. The motion model of multisensor information fusion is analyzed, the dual neural network perturbation model is introduced, and the state variables of the system are determined. Combined with the data structure model of multisensor information fusion, a least-squares-based optimization function is constructed. In this optimization function, the image sensor is constructed as an observationally constrained residual term based on the pixel plane, the inertial measurement unit is constructed as an observationally constrained residual term based on the motion model, and the motion relationship between sensors is constructed as a motion-constrained residual term to derive a mathematical model for real-time multitarget tracking of panoramic video. The simulation experimental results show that the integrated position estimation accuracy of the multisensor information fusion dual neural network-based panoramic video multitarget real-time tracking fusion algorithm is 10.5% higher than that of the optimal distributed panoramic video multitarget fusion algorithm without feedback. The results of the simulation experiments show that the integrated position estimation accuracy of the multitarget real-time tracking fusion algorithm for panoramic video improves by 10.96%, and the integrated speed estimation accuracy improves by 6.32% compared with the optimal distributed multitarget fusion algorithm without feedback. The proposed panoramic video multitarget real-time tracking algorithm based on the dual neural network can effectively improve the target tracking accuracy of the model on degraded frames (motion blur, target occlusion, out-of-focus, etc.), and the stability of the algorithm for target location and category detection is effectively improved by multiframe feature fusion. The research in this paper provides better technical support and research theory for panoramic video multitarget real-time tracking.

Real-Time Multitarget Tracking for Panoramic Video Based on Dual Neural Networks for Multisensor Information Fusion

Searching for available parking spaces can be a painful experience for drivers due to driving around until finding a vacant space. This study proposes a new method to automatically detect available parking spaces. The proposed system identifies empty parking spaces using grayscale images obtained from any type of video camera. The method was found to successfully identify parking availability under different conditions and scenarios. The method was tested using real-life data and achieved a detection rate of 99.7%. This method can be applied in real-time to monitor parking availability and guide drivers to empty spaces. The method has several advantages, including simple algorithms, the use of low-quality black and white images, and simple configuration. Therefore, the system can provide enormous cost savings for locations with existing black and white surveillance cameras instead of replacing existing cameras with new high-quality cameras. • A new method to detect available parking spaces in outdoor parking was proposed. • The proposed method uses grayscale images obtained from any type of video camera. • Using real-life data, the proposed method achieved a detection rate of 99.7%. • The achieved detection rate is higher than most available methods in the literature. 

Parking detection method using quadtree decomposition analysis

In this paper, we study parking slot detection problem for automated parking function. Main contribution is to propose a neural network to detect parking slot based on keypoint estimation, which is called KEPS-NET. The proposed network detects three kinds of parking slot (perpendicular, parallel, and slanted) with two entrance corner points. We first analyze how precise localization of the entrance points in pixel level is important for real automated parking scenario. Then we construct parking slot detector with four heads using a common feature extraction backbone network to estimate center point, entrance points, slot type, and slot angle. And separate key-point detector with two heads is added to find entrance points more accurately by leveraging richer local features. We also design a slope loss helping the network minimize the orientation errors of the entrance line, which allows the network to obtain greatly improved localization accuracy. Through the experimental results, we show that the proposed algorithm achieves state-of-the-art performance compared to the previous works. 

KEPS-NET: Robust Parking slot Detection based Keypoint estimation for High Localization Accuracy

Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In reinforcement learning, rationally reusing the policies acquired from other tasks or human experts is critical for tackling problems that are difficult to learn from scratch. In this work, we present a framework called Selective Myopic bEhavior Control~(SMEC), which results from the insight that the short-term behaviors of prior policies are sharable across tasks. By evaluating the behaviors of prior policies via a hybrid value function architecture, SMEC adaptively aggregates the sharable short-term behaviors of prior policies and the long-term behaviors of the task policy, leading to coordinated decisions. Empirical results on a collection of manipulation and locomotion tasks demonstrate that SMEC outperforms existing methods, and validate the ability of SMEC to leverage related prior policies.

On the Value of Myopic Behavior in Policy Reuse

Due to distortion, limitations of vision, and occlusion, most of the existing vacant parking slot detection methods with a standalone around view monitor (AVM) are prone to miss some parking slots ...

Vacant parking slot detection and tracking during driving and parking with a standalone around view monitor


 The accuracy and sampling rate of a coseismic displacement and velocity waveform can be improved by fusing Global Navigation Satellite System (GNSS) and strong motion (SM) sensor data with a multi-rate Kalman filter. However, many studies have shown that the noise in GNSS coseismic waveforms includes colored noise, and it is challenging to obtain proper GNSS and SM fusion results if the GNSS colored noise is not accurately represented. In this paper, we propose a colored noise multi-rate Kalman filter, which uses a stochastic model for modeling the GNSS colored noise to achieve an accurate fusion of data from GNSS and SM for different sampling rates. In addition, we also propose an estimation method for the multi-rate Kalman filter stochastic model under colored noise to achieve the optimal adaptive fusion of GNSS and SM data. After the reliability of the proposed method was confirmed using Monte Carlo simulations and earthquake engineering data tests, the proposed method was applied to data collected from the 2019 Mw 7.1 Ridgecrest earthquake and 2016 Mw 7.8 Kaikoura earthquake. The test results show that the proposed method can effectively fuse GNSS and SM data and accurately obtain broadband coseismic displacement and velocity waveforms.

Adaptive colored noise multi-rate Kalman filter and its application in coseismic deformations

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On the Value of Myopic Behavior in Policy Reuse

Vacant parking slot detection and tracking during driving and parking with a standalone around view monitor

Adaptive colored noise multi-rate Kalman filter and its application in coseismic deformations