Fast Object Search on Road Networks

In this paper, we present ROAD, a general framework to evaluate Location-Dependent Spatial Queries (LDSQ)s that searches for spatial objects on road networks. By exploiting search space pruning technique and providing a dynamic ob-ject mapping mechanism, ROAD is very efficient and flexible for various types of queries, namely, range search and near-est neighbor search, on objects over large-scale networks. ROAD is named after its two components, namely, Route Overlay and Association Directory, designed to address the network traversal and object access aspects of the frame-work. In ROAD, a large road network is organized as a hier-archy of interconnected regional sub-networks (called Rnets) augmented with 1) shortcuts for accelerating network traver-sals; and 2) object abstracts for guiding traversals. In this pa-per, we present (i) the Rnet hierarchy and several properties useful to construct Rnet hierarchy, (ii) the design and im-plementation of the ROAD framework, (iii) efficient object search algorithms for various queries, and (iv) incremental update techniques for framework maintenance in presence of object and network changes. We conducted extensive ex-periments with real road networks to evaluate ROAD. The experiment result shows the superiority of ROAD over the state-of-the-art approaches

When Do Luxury Cars Hit the Road? Findings by A Big Data Approach

In this paper, we focus on studying the appearing time of different kinds of cars on the road. This information will enable us to infer the life style of the car owners. The results can further be used to guide marketing towards car owners. Conventionally, this kind of study is carried out by sending out questionnaires, which is limited in scale and diversity. To solve this problem, we propose a fully automatic method to carry out this study. Our study is based on publicly available surveillance camera data. To make the results reliable, we only use the high resolution cameras (i.e. resolution greater than $1280 \times 720$). Images from the public cameras are downloaded every minute. After obtaining 50,000 images, we apply faster R-CNN (region-based convoluntional neural network) to detect the cars in the downloaded images and a fine-tuned VGG16 model is used to recognize the car makes. Based on the recognition results, we present a data-driven analysis on the relationship between car makes and their appearing times, with implications on lifestyles

Convolutional Feature Masking for Joint Object and Stuff Segmentation

The topic of semantic segmentation has witnessed considerable progress due to the powerful features learned by convolutional neural networks (CNNs). The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. This strategy introduces artificial boundaries on the images and may impact the quality of the extracted features. Besides, the operations on the raw image domain require to compute thousands of networks on a single image, which is time-consuming. In this paper, we propose to exploit shape information via masking convolutional features. The proposal segments (e.g., super-pixels) are treated as masks on the convolutional feature maps. The CNN features of segments are directly masked out from these maps and used to train classifiers for recognition. We further propose a joint method to handle objects and "stuff" (e.g., grass, sky, water) in the same framework. State-of-the-art results are demonstrated on benchmarks of PASCAL VOC and new PASCAL-CONTEXT, with a compelling computational speed.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201