In this paper, we present Vi-Fi, a multi-modal system that leverages a user's smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e.g. WiFi MAC address). Our approach uses a recurrent multi-modal deep neural network that exploits FTM and IMU measurements along with distance between user and camera (depth information) to learn affinity matrices. As a baseline method for comparison, we also present a traditional non deep learning approach that uses bipartite graph matching. To facilitate evaluation, we collected a multi-modal dataset that comprises camera videos with depth information (RGB-D), WiFi FTM and IMU measurements for multiple participants at diverse real-world settings. Using association accuracy as the key metric for evaluating the fidelity of Vi-Fi in associating human users on camera feed with their phone IDs, we show that Vi-Fi achieves between 81% (real-time) to 91% (offline) association accuracy.

In this paper, we present ViTag to associate user identities across multimodal data, particularly those obtained from cameras and smartphones. ViTag associates a sequence of vision tracker generated bounding boxes with Inertial Mea-surement Unit (IMU) data and Wi-Fi Fine Time Measurements (FTM) from smartphones. We formulate the problem as association by sequence to sequence (seq2seq) translation. In this two-step process, our system first performs cross-modal translation using a multimodal LSTM encoder-decoder network (X-Translator) that translates one modality to another, e.g. recon-structing IMU and FTM readings purely from camera bounding boxes. Second, an association module finds identity matches between camera and phone domains, where the translated modality is then matched with the observed data from the same modality. In contrast to existing works, our proposed approach can associate identities in multi-person scenarios where all users may be performing the same activity. Extensive experiments in real-world indoor and outdoor environments demonstrate that online association on camera and phone data (IMU and FTM) achieves an average Identity Precision Accuracy (IDP) of 88.39% on a 1 to 3 seconds window, outperforming the state-of-the-art Vi-Fi (82.93%). Further study on modalities within the phone domain shows the FTM can improve association performance by 12.56% on average. Finally, results from our sensitivity experiments demonstrate the robustness of ViTag under different noise and environment variations.

Existing surveys look at parking solutions from the perspective of sensors, communication protocols, and the hardware-software interface. While this is a worthwhile approach, it suffers from three obvious shortcomings, namely that present-day sensors are likely to become obsolete in a few years, communication protocols get discontinued, and present-day software will almost certainly not run on tomorrow’s platforms. Consequently, these approaches are not promising for the Smart Cities of the near future. Unlike previous surveys, we look at parking in Smart Cities through the lens of market-based allocation of goods and services. In competitive markets prices act as signals used to allocate goods to those who value them most. In the case of parking spots, some drivers are willing to pay higher prices for the use of those parking spots that offer them the highest utility. What makes our survey unique is that we are looking at the recent literature with an eye for market-oriented solutions including pricing as an instrument for shaping traffic and for incentivizing socially-desirable driver behavior. We believe that one of the important contributions of any survey paper, over and above being a compendium of known art, is to suggest new lines of research. With this in mind, we have peppered the manuscript with slightly unorthodox perspectives. These perspectives are intended to be thought-provoking and to open new avenues for possible investigations.

We demonstrate an application of finding target persons on a surveillance video. Each visually detected participant is tagged with a smartphone ID and the target person with the query ID is highlighted. This work is motivated by the fact that establishing associations between subjects observed in camera images and messages transmitted from their wireless devices can enable fast and reliable tagging. This is particularly helpful when target pedestrians need to be found on public surveillance footage, without the reliance on facial recognition. The underlying system uses a multi-modal approach that leverages WiFi Fine Timing Measurements (FTM) and inertial sensor (IMU) data to associate each visually detected individual with a corresponding smartphone identifier. These smartphone measurements are combined strategically with RGB-D information from the camera, to learn affinity matrices using a multi-modal deep learning network.