Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras

Overlapping cameras offer exciting opportunities to view a scene from different angles, allowing for more advanced, comprehensive and robust analysis. However, existing visual analytics systems for multi-camera streams are mostly limited to (i) per-camera processing and aggregation and (ii) workload-agnostic centralized processing architectures. In this paper, we present Argus, a distributed video analytics system with cross-camera collaboration on smart cameras. We identify multi-camera, multi-target tracking as the primary task of multi-camera video analytics and develop a novel technique that avoids redundant, processing-heavy identification tasks by leveraging object-wise spatio-temporal association in the overlapping fields of view across multiple cameras. We further develop a set of techniques to perform these operations across distributed cameras without cloud support at low latency by (i) dynamically ordering the camera and object inspection sequence and (ii) flexibly distributing the workload across smart cameras, taking into account network transmission and heterogeneous computational capacities. Evaluation of three real-world overlapping camera datasets with two Nvidia Jetson devices shows that Argus reduces the number of object identifications and end-to-end latency by up to 7.13x and 2.19x (4.86x and 1.60x compared to the state-of-the-art), while achieving comparable tracking quality.Comment: 18 pages, under revie

CoMon: Cooperative Ambience Monitoring Platform with Continuity and Benefit Awareness

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications

Understanding customer malling behavior in an urban shopping mall using smartphones

Abstract This paper presents a novel customer malling behavior modeling framework for an urban shopping mall. As an automated computing framework using smartphones, it is designed to provide comprehensive understanding of customer behavior. We prototype the framework in a real-world urban shopping mall. Development consists of three steps; customer data collection, customer trace extraction, and behavior model analysis. We extract customer traces from a collection of 701-hour sensor data from 195 in-situ customers who installed our logging application at Android Market. The practical behavior model is created from the real traces. It has a multi-level structure to provide the holistic understanding of customer behavior from physical movement to service semantics. As far as we know, it is the first work to understand complex customer malling behavior in offline shopping malls

PADA: Power-aware development assistant for mobile sensing applications

� 2016 ACM. We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.N

PowerForecaster: Predicting power impact of mobile sensing applications at pre-installation time

This article presents a hierarchical context monitoring and composition framework that effectively supports next-generation context-aware services. The upcoming ubiquitous space will be covered with innumerable sensors and tiny devices, which ceaselessly pump out a huge volume of data. This data gives us an opportunity for numerous proactive and intelligent services. The services require extensive understanding of rich and comprehensive contexts in real time. The framework provides three hierarchical abstractions: PocketMon (personal), HiperMon (regional), and EGI (global). The framework provides effective approaches to combining context from each level, thereby allowing us to create a rich set of applications, not possible otherwise. It deals with an extensively broad spectrum of contexts, from personal to worldwide in terms of scale, and from crude to highly processed in terms of complexity. It also facilitates efficient context monitoring and addresses the performance issues, achieving a high level of scalability. We have prototyped the proposed framework and several applications running on top of it in order to demonstrate its effectiveness.11Nothe