Enabling Surveillance Cameras to Navigate

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Liang Dong ◽  
Jingao Xu ◽  
Guoxuan Chi ◽  
Danyang Li ◽  
Xinglin Zhang ◽  
...  
Keyword(s):  
Wide Spectrum ◽  
Motion Patterns ◽  
Localization Accuracy ◽  
Fine Grained ◽  
Video Frames ◽  
Key Innovation ◽  
Start Up ◽  
Mobile Cameras ◽  
Fresh Perspective ◽  
Map Function

Smartphone localization is essential to a wide spectrum of applications in the era of mobile computing. The ubiquity of smartphone mobile cameras and surveillance ambient cameras holds promise for offering sub-meter accuracy localization services thanks to the maturity of computer vision techniques. In general, ambient-camera-based solutions are able to localize pedestrians in video frames at fine-grained, but the tracking performance under dynamic environments remains unreliable. On the contrary, mobile-camera-based solutions are capable of continuously tracking pedestrians; however, they usually involve constructing a large volume of image database, a labor-intensive overhead for practical deployment. We observe an opportunity of integrating these two most promising approaches to overcome above limitations and revisit the problem of smartphone localization with a fresh perspective. However, fusing mobile-camera-based and ambient-camera-based systems is non-trivial due to disparity of camera in terms of perspectives, parameters and incorrespondence of localization results. In this article, we propose iMAC, an integrated mobile cameras and ambient cameras based localization system that achieves sub-meter accuracy and enhanced robustness with zero-human start-up effort. The key innovation of iMAC is a well-designed fusing frame to eliminate disparity of cameras including a construction of projection map function to automatically calibrate ambient cameras, an instant crowd fingerprints model to describe user motion patterns, and a confidence-aware matching algorithm to associate results from two sub-systems. We fully implement iMAC on commodity smartphones and validate its performance in five different scenarios. The results show that iMAC achieves a remarkable localization accuracy of 0.68 m, outperforming the state-of-the-art systems by >75%.

A Large-scale Analysis of Hundreds of In-memory Key-value Cache Clusters at Twitter

2021 ◽  
Vol 17 (3) ◽  
pp. 1-35
Author(s):  
Juncheng Yang ◽  
Yao Yue ◽  
K. V. Rashmi
Keyword(s):  
Large Scale ◽  
Production Systems ◽  
Wide Spectrum ◽  
Use Cases ◽  
Scale Analysis ◽  
Business Logic ◽  
Traffic Pattern ◽  
Fine Grained ◽  
Memory Cache ◽  
Large Scale Analysis

Modern web services use in-memory caching extensively to increase throughput and reduce latency. There have been several workload analyses of production systems that have fueled research in improving the effectiveness of in-memory caching systems. However, the coverage is still sparse considering the wide spectrum of industrial cache use cases. In this work, we significantly further the understanding of real-world cache workloads by collecting production traces from 153 in-memory cache clusters at Twitter, sifting through over 80 TB of data, and sometimes interpreting the workloads in the context of the business logic behind them. We perform a comprehensive analysis to characterize cache workloads based on traffic pattern, time-to-live (TTL), popularity distribution, and size distribution. A fine-grained view of different workloads uncover the diversity of use cases: many are far more write-heavy or more skewed than previously shown and some display unique temporal patterns. We also observe that TTL is an important and sometimes defining parameter of cache working sets. Our simulations show that ideal replacement strategy in production caches can be surprising, for example, FIFO works the best for a large number of workloads.

DAFI

2021 ◽  
Vol 5 (4) ◽  
pp. 1-21
Author(s):  
Hang Li ◽  
Xi Chen ◽  
Ju Wang ◽  
Di Wu ◽  
Xue Liu
Keyword(s):  
Indoor Localization ◽  
Domain Adaptation ◽  
Target Domain ◽  
Localization Accuracy ◽  
Source Domain ◽  
Fine Grained ◽  
Time Signals ◽  
Environmental Variations ◽  
Device Free ◽  
Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

scholarly journals RF-SML: A SAR-Based Multi-Granular and Real-Time Localization Method for RFID Tags

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1447
Author(s):  
Yue Jiang ◽  
Yongtao Ma ◽  
Hankai Liu ◽  
Yunlei Zhang
Keyword(s):  
Rapid Development ◽  
Target Position ◽  
Coarse Grained ◽  
Uhf Rfid ◽  
Localization Accuracy ◽  
Fine Grained ◽  
Antenna Phase ◽  
Phase Calibration ◽  
Commercial Off The Shelf ◽  
Antenna Phase Center

With the rapid development of the Internet of Things (IoT) technology, location based service in context awareness has received increasing attention. As one of the main localization technologies, UHF RFID technology has been widely used in many fields of life and industry due to its advantages. In this article, we introduce a RFID-based system RF-SML, which is a method for quickly and accurately locating static objects via the tag and mobile reader. Specifically, the method utilizes the idea of multi-granularity in order to find the high-probability region of the target position by reconstructing the reflection coefficient of the scene in the coarse-grained localization stage. Subsequently, in the fine-grained localization stage, the grid is traversed in this area to calculate the corresponding evaluation factor to determine the final position result, thereby reducing the time-consuming of localization calculation. At the same time, it uses phase calibration to remove the phase offsets that are caused by the hardware device and the antenna phase center, thereby obtaining higher localization accuracy. We conduct experiments to verify the performance of RF-SML with commercial-off-the-shelf (COTS) RFID equipment. The results show that the proposed method can efficiently achieve the centimeter-level positioning of objects.

scholarly journals LEARNING MAPS FOR OBJECT LOCALIZATION USING VISUAL-INERTIAL ODOMETRY

2020 ◽  
Vol V-1-2020 ◽  
pp. 343-350
Author(s):  
B. Zha ◽  
A. Yilmaz
Keyword(s):  
Object Motion ◽  
Spatial Uncertainty ◽  
Object Localization ◽  
Abstract Objects ◽  
Motion Patterns ◽  
Localization Accuracy ◽  
Single Location ◽  
Trajectory Classification ◽  
Potential Motion ◽  
Localization Approach

Abstract. Objects follow designated path on maps, such as vehicles travelling on a road. This observation signifies topological representation of objects’ motion on the map. Considering the position of object is unknown initially, as it traverses the map by moving and turning, the spatial uncertainty of its whereabouts reduces to a single location as the motion trajectory would fit only to a certain map trajectory. Inspired by this observation, we propose a novel end-to-end localization approach based on topological maps that exploits the object motion and learning the map using an recurrent neural network (RNN) model. The core of the proposed method is to learn potential motion patterns from the map and perform trajectory classification in the map’s edge-space. Two different trajectory representations, namely angle representation and augmented angle representation (incorporates distance traversed) are considered and an RNN is trained from the map for each representation to compare their performances. The localization accuracy in the tested map for the angle and augmented angle representations are 90.43% and 96.22% respectively. The results from the actual visual-inertial odometry have shown that the proposed approach is able to learn the map and localize objects based on their motion.

scholarly journals Research on the Production of Composite Fuel on the Basis of Fine-Grained Coal Fractions and Biomass—The Impact of Process Parameters and the Type of Binder on the Quality of Briquettes Produced

Minerals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 31 ◽  
Author(s):  
Michał Rejdak ◽  
Jolanta Robak ◽  
Agata Czardybon ◽  
Karina Ignasiak ◽  
Piotr Fudała
Keyword(s):  
Raw Materials ◽  
Wide Spectrum ◽  
Potato Starch ◽  
Mixing Time ◽  
Extrusion Process ◽  
Cellulose Derivatives ◽  
Fine Grained ◽  
Mechanical Durability ◽  
Materials Used ◽  
The Impact

This paper presents the partial results of a study on obtaining compacted fuel from fine-grained coal fractions and biomass. The aim of the study was to determine the impact of selected parameters of the extrusion process and the applied binder (mechanical durability and density of the products). The fuels were formulated using the extrusion process. Raw materials used in the research were: Fine-grained coal (flotation concentrates), biomass (hydrolytic lignocellulose), and a wide spectrum of organic and mineral binders and their compositions. During the investigations, the variable factors were the following: Extrusion pressure, preparation of the mixtures for extrusion (mixing time and temperature of the mixture), composition of the extruded mixtures (share of fine-grained coal and biomass and type of binder). It was found that it is possible to extrude mechanically durable briquettes from mixtures containing fine-grained coal products and biomass. Under the conditions of the experiment, the most favorable mechanical durability was characterized by briquettes containing in their composition 90% of coal and 6% of biomass (in relation to the dry state). The briquettes with the most favorable physico-mechanical properties were obtained using organic binders—Starch (based on wheat and potato starch) and cellulose derivatives.

scholarly journals Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1015
Author(s):  
Yuqing Yin ◽  
Xu Yang ◽  
Peihao Li ◽  
Kaiwen Zhang ◽  
Pengpeng Chen ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Bayesian Model Averaging ◽  
Indoor Localization ◽  
Domain Adaptation ◽  
Model Averaging ◽  
Indoor Environments ◽  
Test Bed ◽  
Localization Accuracy ◽  
Fine Grained ◽  
Multiple Domains

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

What drives the growth of start-up firms? A tool for mapping the state-of-the-art of the empirical literature

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Roberto Pugliese ◽  
Guido Bortoluzzi ◽  
Marco Balzano
Keyword(s):  
Growth Factors ◽  
Statistical Significance ◽  
Empirical Studies ◽  
Three Dimensional ◽  
Empirical Literature ◽  
Potential Growth ◽  
Content Type ◽  
Fine Grained ◽  
Start Up ◽  
Research Stream

PurposeThis study aims to enrich the current theoretical debate on the growth of start-up firms by extensively investigating the ongoing empirical studies in this research stream. Moreover, this study identifies drivers whose support roles are confirmed in the literature and recommends further research opportunities.Design/methodology/approachIn this study, we analysed the results of 316 empirical studies on start-up firms and growth and also identified and categorised 66 growth drivers. We presented these drivers in three-dimensional charts: 1) the frequency of using each driver in the 316 studies, 2) the consistency of each driver as measured by the number of studies supporting its statistical significance and 3) the net effect (positive or negative) of each driver on growth.FindingsOur analysis compares extant studies on growth drivers and shows some under-explored growth factors of start-up firms.Practical implicationsBoth start-up managers and policymakers can benefit from this study. This study provided managers with a fine-grained tool on the main growth drivers and can guide policymakers in supporting policies for start-up firms.Originality/valueThis study provides a rich, fine-grained and coherent picture of several potential growth drivers of start-up firms. Moreover, we extended our analysis to various potential drivers more than previous studies on this topic, thereby providing fruitful insights into the critical growth factors for start-up firms.

scholarly journals Fine-grained categorization of fish motion patterns in underwater videos

2011 ◽  
Author(s):  
Mohamed Amer ◽  
Emil Bilgazyev ◽  
Sinisa Todorovic ◽  
Shishir Shah ◽  
Ioannis Kakadiaris ◽  
...  
Keyword(s):  
Motion Patterns ◽  
Fine Grained

scholarly journals Att-BiL-SL: Attention-Based Bi-LSTM and Sequential LSTM for Describing Video in the Textual Formation

2021 ◽  
Vol 12 (1) ◽  
pp. 317
Author(s):  
Shakil Ahmed ◽  
A F M Saifuddin Saif ◽  
Md Imtiaz Hanif ◽  
Md Mostofa Nurannabi Shakil ◽  
Md Mostofa Jaman ◽  
...  
Keyword(s):  
Video Data ◽  
Portable Devices ◽  
Multiple Sources ◽  
Video Captioning ◽  
Search Optimization ◽  
Video Frames ◽  
Temporal Features ◽  
Technological Field ◽  
Mobile Cameras ◽  
Textual Formation

With the advancement of the technological field, day by day, people from around the world are having easier access to internet abled devices, and as a result, video data is growing rapidly. The increase of portable devices such as various action cameras, mobile cameras, motion cameras, etc., can also be considered for the faster growth of video data. Data from these multiple sources need more maintenance to process for various usages according to the needs. By considering these enormous amounts of video data, it cannot be navigated fully by the end-users. Throughout recent times, many research works have been done to generate descriptions from the images or visual scene recordings to address the mentioned issue. This description generation, also known as video captioning, is more complex than single image captioning. Various advanced neural networks have been used in various studies to perform video captioning. In this paper, we propose an attention-based Bi-LSTM and sequential LSTM (Att-BiL-SL) encoder-decoder model for describing the video in textual format. The model consists of two-layer attention-based bi-LSTM and one-layer sequential LSTM for video captioning. The model also extracts the universal and native temporal features from the video frames for smooth sentence generation from optical frames. This paper includes the word embedding with a soft attention mechanism and a beam search optimization algorithm to generate qualitative results. It is found that the architecture proposed in this paper performs better than various existing state of the art models.

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