6 research outputs found
Real-Time Illegal Parking Detection System Based on Deep Learning
The increasing illegal parking has become more and more serious. Nowadays the
methods of detecting illegally parked vehicles are based on background
segmentation. However, this method is weakly robust and sensitive to
environment. Benefitting from deep learning, this paper proposes a novel
illegal vehicle parking detection system. Illegal vehicles captured by camera
are firstly located and classified by the famous Single Shot MultiBox Detector
(SSD) algorithm. To improve the performance, we propose to optimize SSD by
adjusting the aspect ratio of default box to accommodate with our dataset
better. After that, a tracking and analysis of movement is adopted to judge the
illegal vehicles in the region of interest (ROI). Experiments show that the
system can achieve a 99% accuracy and real-time (25FPS) detection with strong
robustness in complex environments.Comment: 5pages,6figure
Real-World Anomaly Detection in Video Using Spatio-Temporal Features Analysis for Weakly Labelled Data with Auto Label Generation
Detecting anomalies in videos is a complex task due to diverse content, noisy labeling, and a lack of frame-level labeling. To address these challenges in weakly labeled datasets, we propose a novel custom loss function in conjunction with the multi-instance learning (MIL) algorithm. Our approach utilizes the UCF Crime and ShanghaiTech datasets for anomaly detection. The UCF Crime dataset includes labeled videos depicting a range of incidents such as explosions, assaults, and burglaries, while the ShanghaiTech dataset is one of the largest anomaly datasets, with over 400 video clips featuring three different scenes and 130 abnormal events. We generated pseudo labels for videos using the MIL technique to detect frame-level anomalies from video-level annotations, and to train the network to distinguish between normal and abnormal classes. We conducted extensive experiments on the UCF Crime dataset using C3D and I3D features to test our model\u27s performance. For the ShanghaiTech dataset, we used I3D features for training and testing. Our results show that with I3D features, we achieve an 84.6% frame-level AUC score for the UCF Crime dataset and a 92.27% frame-level AUC score for the ShanghaiTech dataset, which are comparable to other methods used for similar datasets
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
The ability to perform accurate prognosis of patients is crucial for
proactive clinical decision making, informed resource management and
personalised care. Existing outcome prediction models suffer from a low recall
of infrequent positive outcomes. We present a highly-scalable and robust
machine learning framework to automatically predict adversity represented by
mortality and ICU admission from time-series vital signs and laboratory results
obtained within the first 24 hours of hospital admission. The stacked platform
comprises two components: a) an unsupervised LSTM Autoencoder that learns an
optimal representation of the time-series, using it to differentiate the less
frequent patterns which conclude with an adverse event from the majority
patterns that do not, and b) a gradient boosting model, which relies on the
constructed representation to refine prediction, incorporating static features
of demographics, admission details and clinical summaries. The model is used to
assess a patient's risk of adversity over time and provides visual
justifications of its prediction based on the patient's static features and
dynamic signals. Results of three case studies for predicting mortality and ICU
admission show that the model outperforms all existing outcome prediction
models, achieving PR-AUC of 0.891 (95 CI: 0.878 - 0.969) in predicting
mortality in ICU and general ward settings and 0.908 (95 CI: 0.870-0.935) in
predicting ICU admission.Comment: 14 page
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Key-point based tracking for illegally parked vehicle detection
This research aims to develop a target detection and tracking system that can realize real-time video surveillance. The purpose of the research is to realize a monitoring application that can run automatically and intelligently to detect and track illegally parked vehicles. Since the application scenario of the algorithm is a real traffic environment, it must be able to adapt to complex environmental interference, such as drastic changes in lighting conditions, frequent occlusion, and long-term stable tracking.
The thesis shows the detailed design process and test results of the system. This algorithm combines the target detection function based on deep learning network and the multi-object tracking algorithm based on key point matching. The method shown in the thesis focuses on detecting and tracking stationary vehicles in the no parking area. An object detection algorithm based on a deep learning network is used to recognize vehicles. Once the recognized vehicle is defined as an illegally parked vehicle through the determination of its motion state and location, an algorithm based on key-point matching is developed and tracked for this type of vehicle. If the target is still stationary in the no parking area after a period, the system will generate an alarm.
The method was tested in more than 20 hours of video. The video comes from public database and our own. They all show real surveillance scenes, including different time periods of the day and different locations. The test results show that the method achieves 100% in precision (also called positive predictive value), 95% in recall (also known as sensitivity) and 97% in F1 (a measure that combines precision and recall). The results obtained also produce better detection and tracking compared to other comparable methods