Buses are the primary means of public transportation in the city of Rio de Janeiro, carrying around 100 million passengers every month. Recently, real-time GPS coordinates of all operating public buses has been made publicly available - roughly 1 million GPS entries each captured each day. In an initial study, we observed that a substantial number of buses follow trajectories that do not follow the expected behavior. In this paper, we present RioBusData, a tool that helps users identify and explore, through different visualizations, the behavior of outlier trajectories. We describe how the system automatically detects these outliers using a Convolutional Neural Network (CNN) and we also discuss a series of case studies which show how RioBusData helps users better understand not only the flow and service of outlier buses but also the bus system as a whole.
More closely related to RioBusData is the work by @cite_0 . They described a web-based visualization package that summarizes spatial patterns and temporal trends. They proposed data mining algorithms for filtering out data sets to identify spatial outlier patterns which, like RioBusData, were implemented and tested using a real-world traffic data set. RioBusData is different from this work in the sense that the outliers are previously and automatically detected with a Machine Learning model, and the visualizations are used to inspect, understand, and take actions based on the already processed information. Finally, propose a simulation-based method that helps in the visual detection of outliers in spatio-temporal data by adjusting functional boxplots @cite_5 . This work is also different from RioBusData in the sense that the detection of outliers is one of the goals of the visualizations. Besides, the proposed method is not suited for large, continuous streams of data, which is the case of the data set explored in our work.
@cite_0 @cite_5
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There have been increasing research interests in automatically constructing image dataset by collecting images from the Internet. However, existing methods tend to have a weak domain adaptation ability, known as the "dataset bias problem". To address this issue, in this work, we propose a novel image dataset construction framework which can generalize well to unseen target domains. In specific, the given queries are first expanded by searching in the Google Books Ngrams Corpora (GBNC) to obtain a richer semantic description, from which the noisy query expansions are then filtered out. By treating each expansion as a "bag" and the retrieved images therein as "instances", we formulate image filtering as a multi-instance learning (MIL) problem with constrained positive bags. By this approach, images from different data distributions will be kept while with noisy images filtered out. Comprehensive experiments on two challenging tasks demonstrate the effectiveness of our proposed approach. We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences. Arbitrary-oriented text detection in the wild is a very challenging task, due to the aspect ratio, scale, orientation, and illumination variations. In this paper, we propose a novel method, namely Arbitrary-oriented Text (or ArbText for short) detector, for efficient text detection in unconstrained natural scene images. Specifically, we first adopt the circle anchors rather than the rectangular ones to represent bounding boxes, which is more robust to orientation variations. Subsequently, we incorporate a pyramid pooling module into the Single Shot MultiBox Detector framework, in order to simultaneously explore the local and global visual information, which can, therefore, generate more confidential detection results. Experiments on established scene-text datasets, such as the ICDAR 2015 and MSRA-TD500 datasets, have demonstrated the supe rior performance of the proposed method, compared to the state-of-the-art approaches. The goal of this work is to automatically collect a large number of highly relevant natural images from Internet for given queries. A novel automatic image dataset construction framework is proposed by employing multiple query expansions. In specific, the given queries are first expanded by searching in the Google Books Ngrams Corpora to obtain a richer semantic descriptions, from which the visually non-salient and less relevant expansions are then filtered. After retrieving images from the Internet with filtered expansions, we further filter noisy images by clustering and progressively Convolutional Neural Networks (CNN) based methods. To evaluate the performance of our proposed method for image dataset construction, we build an image dataset with 10 categories. We then run object detections on our image dataset with three other image datasets which were constructed by weak supervised, web supervised and full supervised learning, the experimental results indicated the effectiveness of our method is superior to weak supervised and web supervised state-of-the-art methods. In addition, we do a cross-dataset classification to evaluate the performance of our dataset with two publically available manual labelled dataset STL-10 and CIFAR-10.