Illegal Parking Detection Research Articles

Crowd-Sensing Enhanced Parking Patrol Using Sharing Bikes’ Trajectories

Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. The government highly relies on active human efforts to detect illegal parking events. However, such an approach is extremely ineffective to cover a large city since the police have to patrol over the entire city roads. The massive and high-quality sharing bike trajectories from Mobike offer us a unique opportunity to design a ubiquitous illegal parking detection approach, as most of the illegal parking events happen at curbsides and have significant impact on the bike users. The detection result can guide the patrol schedule, i.e., send the patrol policemen to the region with higher illegal parking risks, and further improve the patrol efficiency. Inspired by this idea, three main components are employed in the proposed framework: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trajectory pre-processing</i> , which filters outlier GPS points, performs map-matching, and builds trajectory indexes; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">illegal parking detection</i> , which models the normal trajectories, extracts features from the evaluation trajectories, and utilizes a distribution test-based method to discover the illegal parking events; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">patrol scheduling</i> , which leverages the detection result as reference context, and models the scheduling task as a multi-agent reinforcement learning problem to guide the patrol police. Finally, extensive experiments are presented to validate the effectiveness of illegal parking detection, as well as the improvement of patrol efficiency.

Real-Time Illegal Parking Detection Algorithm in Urban Environments

Currently, illegal parking detection tasks are mainly achieved through manually checking by enforcement officers on patrol or using Closed-Circuit Television (CCTV) cameras. However, these methods either need high human labour costs or demand installation costs and procedures. Therefore, illegal parking detection solutions, which can reduce significant labour and equipment installation costs, are highly demanded. This paper proposes a novel voting based detection algorithm using deep learning networks implemented using in-vehicle cameras to achieve illegal parking detection with multiple offences’ types. Adopting in-vehicle cameras better matches real-world mobile scenarios than using traditional CCTV cameras as this helps enforcement authorities to reduce manpower and installation costs. A well-constructed new dataset with more than 10000 high-quality labelled images with seven object categories is built for illegal parking detection tasks. Additionally, one novel labelling method named “minimal illegal units” is proposed for illegal parking detection. It reduces the time and human labelling costs significantly, achieving a better correlation of a vehicle and its parking type. The experiments have been conducted in the urban areas of Singapore. Furthermore, the illumination robustness test has also been performed to illustrate that the proposed detection algorithm exhibits strong resistance to changing illumination conditions in varied operating environments. Our proposed detection algorithm can provide a benchmark for research in illegal parking detection.

Recognition of Taxi Violations Based on Semantic Segmentation of PSPNet and Improved YOLOv3

Taxi has the characteristics of strong mobility and wide dispersion, which makes it difficult for relevant law enforcement officers to make accurate judgment on their illegal acts quickly and accurately. With the investment of intelligent transportation system, image analysis technology has become a new method to determine the illegal behavior of taxis, but the current image analysis method is still difficult to support the detection of illegal behavior of taxis in the actual complex image scene. To solve this problem, this study proposed a method of taxi violation recognition based on semantic segmentation of PSPNet and improved YOLOv3. (1) Based on YOLOv3, the proposed method introduces spatial pyramid pooling (SPP) for taxi recognition, which can convert vehicle feature images with different resolutions into feature vectors with the same dimension as the full connection layer and solve the problem of repeated extraction of YOLOv3 vehicle image features. (2) This method can recognize two different violations of taxi (blocking license plate and illegal parking) rather than only one. (3) Based on PSPNet semantic segmentation network, a taxi illegal parking detection method is proposed. This method can collect the global information of road condition images and aggregate the image information of different regions, so as to improve the ability to obtain the global information orderly and improve the accuracy of taxi illegal parking detection. The experimental results show that the proposed method has excellent recognition performance for the detection rate of license plate occlusion behavior DR is 85.3%, and the detection rate of taxi illegal parking phenomenon DR is 96.1%.

SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

Outdoor Illegal Parking Detection System Using Convolutional Neural Network on Raspberry Pi

This paper proposes a cost-effective vision-based outdoor illegal parking detection system, iConvPark, to automatize the detection of illegally parked vehicles by providing real-time notification regarding the occurrences and locations of illegal parking cases, thereby improving effectiveness of parking rules and regulations enforcement. The iConvPark is implemented on a Raspberry Pi with the use of Convolutional Neural Network as the classifier to identify illegally parked vehicles based on live parking lot image retrieved via an IP camera. The system has been implemented at a university parking lot to detect illegal parking events. Evaluation results show that our proposed system is capable of detecting illegally parked vehicles with precision rate of 1.00 and recall rate of 0.94, implying that the detection is robust against changes in light intensity and the presence of shadow effects under different weather conditions, attributed to the superiority offered by CNN.

Suspicious human activity recognition: a review

Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.

Accurate Static Region Classification Using Multiple Cues for ARO Detection

This letter proposes an accurate static region classification for detecting abandoned or removed objects (ARO) using multiple cues. Most existing ARO detection approaches show many falsely detected static regions and low ARO detection performance in real situations because they use single cue and a number of pre-defined threshold values. The proposed method presents multiple cues as intensity, motion, and shape to characterize the true static regions and classifies their candidates into true/false static regions using a SVM classifier, which avoids any dependency on pre-defined threshold values. Experimental results show that the proposed method achieved better ARO detection accuracy and lower false detection rate than the existing methods. In addition, the proposed method can be utilized to several practical applications such as illegal parking detection, garbage throwing detection, thief detection, forest fire detection, and camouflaged solider detection.

Multi-Vehicle Tracking Based Traffic Illegal Parking Detection

Vehicle detection and tracking are the foundations of traffic violation punishment. The current methods have more parameters to be adjusted and can not adapt to the complex traffic scenes. For the purpose of practice application, we propose an illegal parking detection algorithm based on vehicle tracking theory. In order to filter the dynamic interferences such as vehicles parking for a long time, moving vehicles overlapping, and the vehicle losing momentarily from the video, a Gaussian mixture model is combined with the correlations among adjacent pixels to update background and eliminate noises. In allusion to complex traffic scenes, a fast multi-object tracking algorithm is presented to track occluded vehicles effectively. This method predicts vehicle trajectory by Kalman filter and achieves real-time object tracking state judgment by using the feature matching matrix. The experimental results show that this method can track multiple moving vehicles effectively and has high detection precision for illegal parking incidents.

Real-Time Illegal Parking Detection in Outdoor Environments Using 1-D Transformation

With decreasing costs of high-quality surveillance systems, human activity detection and tracking has become increasingly practical. Accordingly, automated systems have been designed for numerous detection tasks, but the task of detecting illegally parked vehicles has been left largely to the human operators of surveillance systems. We propose a methodology for detecting this event in real time by applying a novel image projection that reduces the dimensionality of the data and, thus, reduces the computational complexity of the segmentation and tracking processes. After event detection, we invert the transformation to recover the original appearance of the vehicle and to allow for further processing that may require 2-D data. We evaluate the performance of our algorithm using the i-LIDS vehicle detection challenge datasets as well as videos we have taken ourselves. These videos test the algorithm in a variety of outdoor conditions, including nighttime video and instances of sudden changes in weather.