Recently, discriminative correlation filter based trackers have achieved extremely successful results in many competitions and benchmarks. These methods utilise a periodic assumption of the training samples to efficiently learn a classifier. However, this assumption will produce unwanted boundary effects which severely degrade the tracking performance. Correlation filters with limited boundaries and spatially regularised discriminative correlation filters were proposed to reduce boundary effects. However, their methods use the fixed scale mask or pre‐designed weights function, respectively, which are unsuitable for large‐scale variation. In this study, the authors proposed multiscale spatially regularised correlation filters (MSRCF) for visual tracking. The authors’ augmented objective can reduce the boundary effect even in large‐scale variation, leading to more discriminative model. The proposed multiscale regularisation matrix makes MSRCF fast convergence. The authors’ online tracking algorithm performs favourably against state‐of‐the‐art trackers on OTB‐2013 and OTB‐2015 Benchmark in terms of efficiency, accuracy and robustness.

Recently, the concept of barrier-coverage of wireless sensor network has been introduced for various civilian and military defense applications. This paper studies the problem of how to organize hybrid sensor network, which consists of a number of energy-scarce ground sensors with homogenous initial battery level and energy-plentiful mobile sensors, to maximum the lifetime of barrier-coverage. Two key observations are (a) as the lifetime of each mobile sensor is much longer than that of the static ground sensors, each mobile sensor is capable of contributing multiple sensor barrier formations, and (b) no mobile sensor node can join two hybrid barriers which will be successively used to continuously protect the area of interest due to the moving delay. Based on these, we introduce a new maximum lifetime barrier-coverage problem in hybrid sensor network. We first propose a simple heuristic algorithm by combining existing ideas along with our own. Then, we design another efficient algorithm for the problem and prove that the lifetime of hybrid barrier constructed by this algorithm is at least three times greater than the existing one on average. Our simulation result shows that the second algorithm outperforms the first algorithm at least 33 percent and up to 100 percent.

Following the recent advances in drone technologies, various algorithmic optimization problems related to the effective operation of drones are drawing lots of attentions. This paper considers two interesting multiple-drone-assisted search-and-reconnaissance scenarios, in each of which, the trajectory optimization of multiple drones is of great significance to minimize the latency in the system. In the first scenario, multiple drones, whose moments of mobilization are not necessarily the same, are trying to urgently collect intelligence from a given point of interest, and we would like to minimize the task completion time, i.e., the time period between the moment that the first drone commences its operation to the moment that the intelligence from all of the points are collected, by optimizing their trajectories. In the second scenario, multiple drones with different speeds, are hovering around the same routes to regularly collect intelligence from highly geographically-diversified points of interest over an extended time period, and we would like to minimize the worst-case data refreshment rate, the largest time gap between two consecutive observations over the same point of interest. In this paper, we formally define each problem, prove its NP-hardness, and propose an approximation algorithm for it. We also conduct a simulation to study the performance of our result.

Recently an online electric vehicle (OLEV) concept has been introduced, where vehicles are propelled by the wirelessly transmitted electrical power from the infrastructure installed under the road while moving. The absence of secure-and-fair billing is one of the main hurdles to widely adopt this promising technology. This paper introduces a new secure and privacy-aware fair billing framework for OLEV on the move through the charging plates installed under the road. We first propose two extreme lightweight mutual authentication mechanisms, a direct authentication and a hash chain-based authentication between vehicles and the charging plates that can be used for different vehicular speeds on the road. Second, we propose a secure and privacy-aware wireless power transfer on move for the vehicles with bidirectional auditability guarantee by leveraging game theoretic approach. Each charging plate transfers a fixed amount of energy to the vehicle and bills the vehicle in a privacy-aware way accordingly. Our protocol guarantees secure, privacy-aware, and fair billing mechanism for the OLEVs while receiving electric power from the infrastructure installed under the road. Moreover, our proposed framework can play a vital role in eliminating the security and privacy challenges in the deployment of power transfer technology to the OLEVs.

Location based services (LBSs), which are useful applications of mobile online social network (mOSN), exploit various geographic properties. Location sharing helps people to share their current locations with designated friends and is one important primitive to construct the LBSs. The recent reports showed that a poorly designed location sharing scheme could easily allow the privacy of users to be violated. Over years, lots of efforts are made to provide a privacy-preserving location sharing, but none of them is satisfactory. To address this issue, we introduce a new location sharing scheme in mOSNs with a strong user privacy protection mechanism such that (a) the user's current location as well as (b) the list of friends who will learn the user's current location will be protected from any unintended entity, while the designated friends in the list will learn the exact location of the user. For this purpose, we introduce a new cryptography primitive called the functional pseudonym scheme based on Lagrange polynomial with the public social network IDs of the designated friends. Then, the pseudonym of a user is posted on the server along with the current location of the user. While each user can see every posted messages (pseudonym and location pairs), the actual identify of the originator of each pair can be verified only by designated friends, whose identities are used to compute the pseudonym. Most importantly, unlike any of the existing counterparts, our scheme does not assume neither a trusted server nor pre-established secret among the friends.

Over years, virtual backbone has attracted lots of attention as a promising approach to deal with the broadcasting storm problem in wireless networks. Frequently, the problem of a quality virtual backbone is formulated as a variation of the minimum connected dominating set problem. However, a virtual backbone computed in this way is not resilient against topology change since the induced graph by the connected dominating set is one-vertex-connected. As a result, the minimum k-connected m-dominating set problem is introduced to construct a fault-tolerant virtual backbone. Currently, the best known approximation algorithm for the problem in unit disk graph by Wang assumes k ≤ 3 and m ≥ 1, and its performance ratio is 280 when k = m = 3. In this paper, we use a classical result from graph theory, Tutte decomposition, to design a new approximation algorithm for the problem in unit disk graph for k ≤ 3 and m ≥ 1. In particular, the algorithm features with (a) a drastically simple structure and (b) a much smaller performance ratio, which is nearly 62 when k = m = 3. We also conduct simulation to evaluate the performance of our algorithm.

Lane classification is a fundamental problem for autonomous driving and map-aided localization. Many existing algorithms rely on special designed 1D or 2D filters to extract features of lane markings from either color images or LiDAR data. However, these handcrafted features could not be robust under various driving and lighting conditions.

Recently a new concept of online electric vehicle (OLEV) has been introduced in South Korea, where vehicles are propelled through the transmitted energy from the infrastructure installed underneath the road. However, for billing and audit reasons only authentic vehicles with necessary credentials are allowed to charge their batteries and pay the designated amount to the service provider. Moreover, due to the massive budget requirements for such infrastructure, only designated road segments will offer the charging service. As a result, a tradeoff solution to the charging of electric vehicles is needed to both fulfill the charging requirements of the electric vehicles and reduce the upfront costs for the service providers. To obtain electric charge from the charging plates beneath the road, vehicles need to authenticate themselves beforehand for twofold purposes: to bill the vehicles accordingly and to let the revocation authorities revoke the vehicle in case of a dispute. In this paper, we use the core concept of the OLEV and introduce extreme lightweight privacy-aware authentication schemes for charging-on-the-move through the charging plates installed under the road. More precisely we propose two mutual authentication mechanisms between charging plates and the vehicles, a direct authentication and a hash chain-based authentication. In the direct authentication scheme, we leverage multiple pseudonyms for conditional privacy. Vehicles use different pseudonyms every time they use the charging-on-the-move service. Whereas in case of hash chain-based authentication mechanism, the vehicles mutually authenticate with charging plates through service provider. Our proposed authentication mechanisms preserve conditional privacy throughout the protocol and is computationally lightweight than the existing mechanisms.

In this paper, we describe a novel algorithm that counts and classifies highway vehicles based on regression analysis. This algorithm requires no explicit segmentation or tracking of individual vehicles, which is usually an important part of many existing algorithms. Therefore, this algorithm is particularly useful when there are severe occlusions or vehicle resolution is low, in which extracted features are highly unreliable. There are mainly two contributions in our proposed algorithm. First, a warping method is developed to detect the foreground segments that contain unclassified vehicles. The common used modeling and tracking (e.g., Kalman filtering) of individual vehicles are not required. In order to reduce vehicle distortion caused by the foreshortening effect, a nonuniform mesh grid and a projective transformation are estimated and applied during the warping process. Second, we extract a set of low-level features for each foreground segment and develop a cascaded regression approach to count and classify vehicles directly, which has not been used in the area of intelligent transportation systems. Three different regressors are designed and evaluated. Experiments show that our regression-based algorithm is accurate and robust for poor quality videos, from which many existing algorithms could fail to extract reliable features.

A wireless sensor network is said to form a belt-barrier for a region if it is able to detect any object moving from outside the region to inside. Recently, Cheng and Tsai found if camera sensors are used to form a belt-barrier, the breadth of the barrier becomes an important quality factor to ensure high quality of monitoring (QoM). Then, they proposed the minimum β-breadth belt-barrier construction problem ((β,1)-B3CP) whose goal is to select a minimum number of camera sensors to form a β-breadth belt-barrier, which ensures the width of the picture of any object which moves through the barrier is at least β. In this paper, we perform more thorough investigation of the problem and introduce a new polynomial time exact algorithm for the problem under the assumption that the angle of each camera is fixed. Our simulation result shows our algorithm outperforms Cheng and Tsai's algorithm. We also introduce a variation of (β, 1)-B3CP, namely (β, k)-B3CP, which aims to construct k node-disjoint β-breadth belt-barrier for fault-tolerance purpose, propose a new heuristic algorithm for it, and conduct simulations to evaluate its performance.

This paper presents a novel system framework of face beautification. Unlike prior works that deal with single images, the proposed beautification framework is designed for an input video and it is able to improve both the appearance and the shape of a face. Our system adopts a state-of-the-art algorithm to synthesize and track 3D face models using blendshapes. The personalized 3D model can be edited to satisfy personal preference. This interactive process is needed only once per subject. Based on the tracking result and the modified face model, we present an algorithm to beautify the face video efficiently and consistently. Furthermore we develop a variant of content preserving warping to reduce warping distortions along the face boundary. Finally we adopt real time bilateral filtering to remove wrinkles, freckles, and unwanted blemishes. This framework is evaluated on a set of videos. The experiments demonstrate that our framework can generate consistent and pleasant results over video frames while the original expressions and features are persevered naturally.

Iris segmentation is an important module of iris recognition that can substantially affect recognition performance. Since iris and pupil boundaries usually are not exactly circular, spline-based methods have been used to model irregular iris and pupil boundaries recently. However, in most existing methods, many other factors or modules in the iris recognition pipeline are evaluated together and their mixed effects are assumed to be negligible. More importantly, the splines that model irregularity of the boundaries could not be enough to model the internal nonlinear deformations of an iris pattern (e.g., caused by iris dilation). As a result, it remains unclear whether spline-based methods can provide significant improvements. In this paper, we conduct a complete performance comparison between circular and spline-based methods. There are mainly two contributions. Firstly, for the purpose of comparison, we propose a spline estimator that is robust to outliers caused by eyelashes, eyelids, highlights, and shadows. Secondly, we analyze the relation between iris matching distances and segmentation results by using circular and spline-based methods. Based on our experiments, we found that, even with the proposed robust spline estimator, the improvement of recognition performance is still limited (around 6%). Therefore, in case that less robust spline estimators are used due to the real-time requirement in practical systems, the actual recognition improvement by using splines could be far below the expectation.

This paper investigates two new groups of trajectory optimization problems which stem from networked multi-robotic systems. In particular, we study how to efficiently collect data from stationary sensor nodes using multiple robotic vehicles such as data ferries under different circumstance. The first group includes two new problems which aim to find the tours and the paths, respectively, of k robot vehicles with different mobilization conditions to collect data from ground sensor nodes with minimum latency. The second group consists of one new problem whose goal is to determine the quality tours of k robot vehicles with different speeds, where each of which follows its corresponding tour to repeatedly collect data from stationary sensors. We prove the three problems are NP-hard and propose constant factor approximation strategies for them. Through a simulation, an analytical study is conducted to evaluate the average performance of our core contribution.

The term "smart grid" refers to the next generation power supply system. A smart meter, an essential component of the grid system, is installed at each housing unit and acts as an agent for the unit. While the smart meter is a key enabler of great opportunities and conveniences in smart grid, it is susceptible to various cyber-security attacks, especially privacy invasion from electricity providers. Trusted third party (TTP) and homomorphic encryption are two favorite tools to deal with this issue in the literature. Unfortunately, the use of TTP does not completely eliminate the privacy risk. On the other hand, the use of homomorphic encryption makes it harder for the providers to support various services whose demand can be highly diversified. In this paper, we introduce a drastically new approach to deal with the consumer privacy issue in smart grid. Our key idea is let each consumer to determine the frequency of the measurement report. In this way, each consumer can responsibly make a trade-off between the level of privacy preservation with the quality of the services it will receive.

This paper investigates the problem of computing the optimal trajectories of multiple data MULEs (e.g., robots, vehicles, etc.) to minimize data collection latency in wireless sensor networks. By relying on a slightly different assumption, we define two interesting problems, the k-traveling salesperson problem with neighborhood ( k-TSPN) and the k-rooted path cover problem with neighborhood ( k-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them along with two simpler heuristic algorithms. We also conduct simulations to compare the performance of the proposed approaches with the existing alternatives. Our simulation results indicate that the proposed algorithms outperform the competitors on average.

This paper introduces two new coverage problems in mission-driven camera sensor networks, namely the target temporal effective-sensing coverage with non-adjustable cameras (TEC-NC) problem and the target-temporal effective-sensing coverage with adjustable cameras (TEC-AC) problem. Given a mission period, the objective of the problems is to find a sleep-wakeup schedule of the camera sensor nodes such that the overall target-temporal coverage is maximized. We formally introduce a method called Identifiability Test to check if a target with a face direction is effectively-covered by a camera sensor, and prove the problems are NP-hard. For TEC-NC, we propose a 2-approximation algorithm and two heuristic algorithms. We also design a greedy strategy which can be combined with our solutions for TEC-NC to solve TEC-AC. The simulation results indicate the quality of the outputs of our algorithms are much better than that of the existing alternative as well as close to the theoretical optimum on average.

As iris recognition systems have been deployed in many security areas, liveness detection that can distinguish between real iris patterns and fake ones becomes an important module. Most existing algorithms focus on the appearance difference between real and fake iris (for example, printed patterns, cosmetic contact lenses etc.) which is a very difficult problem. Instead of studying image properties of fake irises, we show that pupil constriction, the fundamental characteristic of real and live irises, can be very robust for liveness detection. In this experimental study, we first build an iris acquisition system that can acquire two eye images under two different illumination conditions in a less intrusive environment. Second, in order to model the process of pupil constriction, we propose a feature descriptor that consists of similarity measurement between iris patches and ratio of iris and pupil diameters. Third, the performance of liveness prediction is evaluated based on the training of a Support Vector Machine (SVM) classifier. The high success prediction rate shows that the classifier is effective without knowing any prior knowledge of fake irises.

This paper identifies a new security problem of existing scheduling algorithms for barrier-coverage of sensors, which never considered before. A barrier-cover of wireless sensors is a subset of sensors seamlessly spanning between two opposite sides such that no intruder can move from one side to the other without being detected. The goal of the scheduling algorithms is to find a sleep-wakeup schedule of sensors such that the time to protect an area of interest using a series of alternating barrier-covers can be maximized. We introduce a new security problem which may exist when two barrier-covers, whose covered areas are not completely disjoint, alternate. We show how an intruder can utilize a set of points, namely “barrier-breaches”, to penetrate the alternating barrier-covers. We also propose two remedies for this problem for existing scheduling algorithms. Our analysis shows that depending on the input graph, one of our approaches works better than the other. Given that such scheduling algorithms only need to run during the initialization phase of a sensor network, we suggest to apply both approaches and pick the better schedule rather than relying solely on the approach which works well on average.

This paper considers the problem of computing the optimal trajectories of multiple mobile elements (e.g. robots, vehicles, etc.) to minimize data collection latency in wireless sensor networks (WSNs). By relying on slightly different assumption, we define two interesting problems, the k-traveling salesperson problem with neighborhood (k-TSPN) and the k-rooted path cover problem with neighborhood (k-PCPN). Since both problems are NP-hard, we propose constant factor approximation algorithms for them. Our simulation results indicate our algorithms outperform their alternatives.

This paper discusses an approach to mo- bile robot path planning which utilizes a wavefront- like propagation method for determining near-optimal paths. This approach is different from previous wave propagation methods in three aspects: it attempts to find near-optimal paths from all locations in the free space to the destination instead of one optimal path from the source location to the destination, may per- form repeated propagations to converge to a solution, and is composed of simplified computations based on local information to facilitate a VLSI implementation. I. INTRODUCTION Wavefront propagation methods for robot path plan- ning have been proposed by other researchers. The tradi- tional approach is to start with a list containing only the destination point (or a region representing a small area around the destination point) and propagate a value rep- resenting the cost of traveling between the two regions to each unvisited neighbor of the region. The region is removed from the list and all its newly visited neighbors are added to the list. The list is sorted by increasing cost and the process is repeated with the lowest cost region in the list. This process terminates when the source region is reached. The actual path is then constructed between the endpoints using the computed cost values. Jahanbin and Fallside(l) have presented some wavefront propagation path planning algorithms consisting only of free-space and obstacles in configuration space. They pre- sented two solutions to the planning problem, one for uniform region sizes and another for non-uniform region sizes. The propagation was performed by the use of a mask which was placed over successive positions on the wavefront. Various shape wavefronts could be propagated depending on the weights in the mask. Once the propa- gation was completed the path was constructed from the source to the lowest cost neighbor and this process was repeated until the destination was reached. There are two

This work explores a method for path planning for a robot in an environment that consists of stationary and moving obstacles. The technique uses binary partitioning of the environment by the obstacles to establish and analyze the spatial relationship between robot and obstacles. Hyperplanes are used to obtain recursively a disjoint set of D-dimensional cells, with each cell designated as belonging to either the interior or the exterior of the set. An overt idea of binary space partitioning as a representation of polytopes is given. Thus, the space-time configuration of free space is viewed as disjoint polytopes; each point in space-time is considered a map into a unique polytope. A traversal of the tree is used to obtain a linear order which gives the collision probability with the environment. Adjacent polytopes between the start and goal polytopes are then utilized to obtain a trajectory. Current implementation for a 2D static environment is discussed.>

The problem of finding a path for a mobile robot in two dimensions among stationary obstacles using a free-space graph representation of the robot's workspace is examined. One problem with solutions of this type has been the time it takes to construct a graph from the image of the workspace. Two solutions are presented which seek to resolve this situation by constructing the graph representation 'on the fly' in a scanline fashion by processing the image one line at a time from top to bottom. Each method consists of three processes: one which breaks the obstacles in the image into pseudo-obstacles, another which analyzes the pseudo-obstacles and locates regions in the free space which represent nodes in the graph, and a third which completes the graph by locating similarities of the free space regions and uses them as edges. The modularity lends itself toward pipelined processing to reach the solution. Discussions are presented and results given of the graph construction process and the paths found on sample images as well as comparative times.>

We present a method for generating shadows in a static environment represented by polygonal or parametric surfaces, and illuminated by one or more movable point light sources. Concepts of ray tracing are modified and utilized in shadow determination. Ray-patch intersection are determined by using contour integration rather than purely numerical or subdivision techniques and thus the expense of long computation is somewhat lessened. Preprocessing steps are used to ascertain that the number of such calculations are limited. Shadow pairs are detected and pairs that cannot interact to produce shadows are also detected and discarded. Coherence properties are used to limit the number of rays processed. The approach combines elements of the shadow z-buffer in the generation process and to a lesser extent, that of projected polygons in scanline rendering as a preprocessing step. The algorithm is easily integrated with scanline a z-buffer algorithm and thus retains the benefits of a z- buffer. Distributed light sources are modelled as arrays of point light sources and the method allows the computation of umbra-penumbra effects.