Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

A Differential Approach for Gaze Estimation

Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as biases which are subject dependent. Therefore, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to his/her actual gaze. In this paper, we introduce a novel image differential method for gaze estimation. We propose to directly train a differential convolutional neural network to predict the gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by allowing the comparison between two eye images, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or when the latter methods are followed by subject specific gaze adaptation.Comment: Extension to our paper A differential approach for gaze estimation with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Inventory management of the refrigerator\u27s produce bins using classification algorithms and hand analysis.

Tracking the inventory of one’s refrigerator has been a mission for consumers since the advent of the refrigerator. With the improvement of computer vision capabilities, automatic inventory systems are within reach. One inventory area with many potential benefits is the fresh food produce bins. The bins are a unique storage area due to their deep size. A user cannot easily see what is in the bins without opening the drawer. Produce items are also some of the quickest foods in the refrigerator to spoil, despite being temperature and humidity controlled to have the fruits and vegetables last longer. Allowing the consumer to have a list of items in their bins could ultimately lead to a more informed consumer and less food spoilage. A single camera could identify items by making predictions when the bins are open, but the camera would only be able to “see” the top layer of produce. If one could combine the data from the open bins with information from the user as they placed and removed items, it is hypothesized that a comprehensive produce bin inventory could be created. This thesis addresses the challenges presented by getting a full inventory of all items within the produce bins by observing if the hand can provide useful information. The thesis proposes that all items must go in or out of the refrigerator by the main door, and by using a single camera to observe the hand-object interactions, a more complete inventory list can be created. The work conducted for this hand analysis study consists of three main parts. The first was to create a model that could identify hands within the refrigerator. The model needed to be robust enough to detect different hand sizes, colors, orientations, and partially-occluded hands. The accuracy of the model was determined by comparing ground truth detections for 185 new images to the model versus the detections made by the model. The model was 93% accurate. The second was to track the hand and determine if it was moving in or out of the refrigerator. The tracker needed to record the coordinates of the hands to provide useful information on consumer behavior and to determine where items are placed. The accuracy of the tracker was determined by visual inspection. The final part was to analyze the detected hand to determine if it is holding a type of produce or empty, and track if the produce is added or removed from the refrigerator. As an initial proof-of-concept, a two types of produce, an apple and an orange, will be used as a testing ground. The accuracy of the hand analysis (e.g., hand with apple or orange vs. hand empty) was determined by comparing its output to a 301-frame video with ground truth labels. The hand analysis system was 87% accurate classifying an empty hand, 85% accurate on a hand holding an apple, and 74% accurate on a hand holding an orange. The system was 93% accurate at detecting what was added or removed from the refrigerator, and 100% accurate determining where within the refrigerator the item was added or removed

Detection of advanced persistent threat using machine-learning correlation analysis

As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale. APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss. The accurate detection and prediction of APT is an ongoing challenge. This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way. The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps. The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack. MLAPT is experimentally evaluated and the presented sy