Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices

Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers object recognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling. Experiments with Android smartphones and Google Glass over Verizon, AT&T, and a campus Wi-Fi network show that with hardware face detection support (available on many mobile devices), Glimpse achieves precision between 96.4% to 99.8% for continuous face recognition, which improves over a scheme performing hardware face detection and server-side recognition without Glimpse's techniques by between 1.8-2.5×. The improvement in precision for face recognition without hardware detection is between 1.6-5.5×. For road sign recognition, which does not have a hardware detector, Glimpse achieves precision between 75% and 80%; without Glimpse, continuous detection is non-functional (0.2%-1.9% precision)

EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices

In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring. As a result, an increasing number of AI-enabled applications are being developed targeting ubiquitous and mobile devices. While deep neural networks (DNNs) are getting bigger and more complex, they also impose a heavy computational and energy burden on the host devices, which has led to the integration of various specialized processors in commodity devices. Given the broad range of competing DNN architectures and the heterogeneity of the target hardware, there is an emerging need to understand the compatibility between DNN-platform pairs and the expected performance benefits on each platform. This work attempts to demystify this landscape by systematically evaluating a collection of state-of-the-art DNNs on a wide variety of commodity devices. In this respect, we identify potential bottlenecks in each architecture and provide important guidelines that can assist the community in the co-design of more efficient DNNs and accelerators.Comment: Accepted at MobiSys 2019: 3rd International Workshop on Embedded and Mobile Deep Learning (EMDL), 201

Phone-to-Phone Communication for Adaptive Image Classification

In this paper, we present a novel technique for adapting local image classifiers that are applied for object recognition on mobile phones through ad-hoc network communication between the devices. By continuously accumulating and exchanging collected user feedback among devices that are located within signal range, we show that our approach improves the overall classification rate and adapts to dynamic changes quickly. This technique is applied in the context of PhoneGuide – a mobile phone based museum guidance framework that combines pervasive tracking and local object recognition for identifying a large number of objects in uncontrolled museum environments

Pembuatan Aplikasi Pembacaan Quick Response Code Menggunakan Perangkat Mobile Berbasis J2ME Untuk Identifikasi Suatu Barang

Barcode is a symbol marking the real object made of sticks pattern of black and white color for easy recognition by computer. In general, barcode labeling techniques are divided into two groups, namely linear barcodes and barcode 2D (two dimensional). 2D barcode standard has many variations, but most others are superior to standard 2D barcode in Japan found that Quick Response Code (QR Code). In its development until now, mobile devices such as mobile phones have many additional features including an integrated digital camera, a network connection using infrared and bluetooth to the specific operating system. With the presence of the operating system on mobile phones allow developers to create applications that are reliable. Based on the characteristics of the QR Code and the technology used by mobile devices, at the end of this task is made an application that can read data from the image of QR Code catches an integrated camera on J2ME-based mobile devices. The process begins with the arrest of reading QR Code image using the integrated camera on the phone and then made the image and binerisasi process continued with the process of reading QR Code symbols from the binary image. In the process of image used binerisasi Quick Adaptive Thresholding algorithm, this is because the algorithm was able to overcome the image that binerisasi for uneven lighting. From the test results and analysis of reading QR Code with sizes 100x100,150x150 ,200x200 and 300x300 obtained results that the QR Code can be accessed using mobile devices, especially on the Nokia 6300 and nokia E71 is stable in size 200x200. Keywords: decoding, QR Code, the mobile device

Aggregating Local Descriptors for Epigraphs Recognition

In this paper, we consider the task of recognizing epigraphs in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 epigraphs, we used a k-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in evaluating state-of-the-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating SIFT descriptors is the best choice for this task.The Fourth International Conference on Digital Presentation and Preservation of Cultural and Scientific Heritage—DiPP2014 is supported by the Ministry of Education and Science and is under the patronage of UNESCO