Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear

A comparison of audio-based deep learning methods for detecting anomalous road events

Road surveillance systems have an important role in monitoring roads and safeguarding their users. Many of these systems are based on video streams acquired from urban video surveillance infrastructures, from which it is possible to reconstruct the dynamics of accidents and detect other events. However, such systems may lack accuracy in adverse environmental settings: for instance, poor lighting, weather conditions, and occlusions can reduce the effectiveness of the automatic detection and consequently increase the rate of false or missed alarms. These issues can be mitigated by integrating such solutions with audio analysis modules, that can improve the ability to recognize distinctive events such as car crashes. For this purpose, in this work we propose a preliminary analysis of solutions based on Deep Learning techniques for the automatic identification of hazardous events through the analysis of audio spectrograms

Distributed Processing and Analytics of IoT data in Edge Cloud

Sensors of different kinds connect to the IoT network and generate a large number of data streams. We explore the possibility of performing stream processing at the network edge and an architecture to do so. This thesis work is based on a prototype solution developed by Nokia. The system operates close to the data sources and retrieves the data based on requests made by applications through the system. Processing the data close to the place where it is generated can save bandwidth and assist in decision making. This work proposes a processing component operating at the far edge. The applicability of the prototype solution given the proposed processing component was illustrated in three use cases. Those use cases involve analysis performed on values of Key Performance Indicators, data streams generated by air quality sensors called Sensordrones, and recognizing car license plates by an application of deep learning