Abdelhamid Mammeri

A Survey of Image Compression Algorithms for Visual Sensor Networks

With the advent of visual sensor networks (VSNs), energy-aware compression algorithms have gained wide attention. That is, new strategies and mechanisms for power-efficient image compression algorithms are developed, since the application of the conventional methods is not always energy beneficial. In this paper, we provide a survey of image compression algorithms for visual sensor networks, ranging from the conventional standards such as JPEG and JPEG2000 to a new compression method, for example, compressive sensing. We provide the advantages and shortcomings of the application of these algorithms in VSN, a literature review of their application in VSN, as well as an open research issue for each compression standard/method. Moreover, factors influencing the design of compression algorithms in the context of VSN are presented. We conclude by some guidelines which concern the design of a compression method for VSN.

Modeling and Adapting JPEG to the Energy Requirements of VSN

We address the problem of modeling and adapting JPEG to the energy requirements of visual sensor networks (VSN). For JPEG modeling purposes, we develop a simplified high-level energy consumption model for each stage of JPEG-like scheme, which can be used to roughly evaluate the energy dissipated by a given visual sensor. This model is based on the basic operations needed at each stage of JPEG, and it does not take into account the complexity of implementation. For JPEG adaptation, we propose to process only a reduced part of each block of 8times8 DCT coefficients of the target image, which minimizes the dissipated energy and maximizes the system lifetime, while preserving an adequate image quality at the sink.

A geometry-based coverage strategy over urban VANETs

Vehicular ad hoc networks have emerged as a promising field in wireless networking research. Unlike traditional wireless sensor networks, vehicular networks demand more consideration due to their assorted road topology, the high mobility of vehicles and the irregularly placed feasible region of deployment. As one of the most complex issues in vehicular networks, coverage strategy has been researched extensively, especially in complex urban scenarios. However, most existing coverage approaches are based on an ideal traffic map consisting of straight lines and nodes. These simplifications misrepresent the road networks. In order to provide more realistic vehicular networks deployment, this paper proposes a geometry-based coverage strategy to handle the deployment problem over urban scenarios. By taking the shape and area of road segments into account, our scheme suits different kinds of road topology and effectively solves the maximum coverage problem. To evaluate the effectiveness of our scheme, we compare this coverage strategy with α-coverage algorithm. The simulation result verifies that geometry-based coverage strategy culminates in a higher coverage ratio and a lower drop rate than α-coverage under the same constraints. The results also show that the deployment of Road Side Units (RSUs) in regions with high traffic flow is able to cover the majority of communication, so that less RSUs are able to provide better communication performance.

A real-time lane marking localization, tracking and communication system

In this paper, we present an in-vehicle computing system capable of localizing lane markings and communicating them to drivers. To the best of our knowledge, this is the first system that combines the Maximally Stable Extremal Region (MSER) technique with the Hough transform to detect and recognize lane markings (i.e., lines and pictograms). Our system begins by localizing the region of interest using the MSER technique. A three-stage refinement computing algorithm is then introduced to enhance the results of MSER and to filter out undesirable information such as trees and vehicles. To achieve the requirements of real-time systems, the Progressive Probabilistic Hough Transform (PPHT) is used in the detection stage to detect line markings. Next, the recognition of the color and the form of line markings is performed; this it is based on the results of the application of the MSER to left and right line markings. The recognition of High-Occupancy Vehicle pictograms is performed using a new algorithm, based on the results of MSER regions. In the tracking stage, Kalman filter is used to track both ends of each detected line marking. Several experiments are conducted to show the efficiency of our system.

An efficient animal detection system for smart cars using cascaded classifiers

Animal-Vehicle Collisions (AVCs) have been a challenging problem since the creation of cars. Consequently, such collisions cause hundreds of human and animal deaths, thousands of injuries, and billions of dollars in property damage every year. To cope with this challenge, vehicles have to be equipped with smart systems able to detect animals (e.g., moose), which cross roadways, and warn drivers about the imminent danger. In this paper, we develop a new animal detection system following two criteria: detection accuracy and detection speed. To achieve these requirements, a two-stage strategy system is investigated. In the first stage, we use the LBP-Adaboost algorithm which supplies the second stage by a set of ROIs containing moose and other similar-objects. Whereas the second stage is based on an adapted version of HOG-SVM classifier. In this stage, the non-moose ROIs are rejected. To train and test our system, we create our own dataset, which is frequently updated by adding new images. Through an extensive set of simulations, we show that our system is able to detect more than 83% of moose.

Towards a novel trust-based opportunistic routing protocol for wireless networks

AbstractOpportunistic routing is a promising research area in the context of wireless network communications. Security and trustworthy of routing in this field, however, needs to be considerably researched . In this paper, a novel trust establishment algorithm is proposed, designed, and implemented specifically for opportunistic routing protocols which benefits from direct interactions between wireless nodes. The proposed trust model benefits from a novel watchdog mechanism considering not only forwarding behaviour of nodes but also the quality of links between them. Furthermore, three different metrics for next hop selection is introduced enabling nodes to select their next hop forwarders more sophisticatedly using quality of links, geographical location of nodes, and their trust level. Extensive simulation results represent that proposed model can significantly improve the performance of network communications when malicious nodes try to collapse the system.

Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter

We present a novel lane detection and tracking system using a fusion of Maximally Stable Extremal Regions (MSER) and Progressive Probabilistic Hough Transform (PPHT). First, MSER is applied to obtain a set of blobs including noisy pixels (e.g., trees, cars and traffic signs) and the candidate lane markings. A scanning refinement algorithm is then introduced to enhance the results of MSER and filter out noisy data. After that, to achieve the requirements of real-time systems, the PPHT is applied. Compared to Hough transform which returns the parameters ρ and Θ, PPHT returns two end-points of the detected line markings. To track lane markings, two kalman trackers are used to track both end-points. Several experiments are conducted in Ottawa roads to test the performance of our framework. The detection rate of the proposed system averages 92.7% and exceeds 84.9% in poor conditions.

North-American speed limit sign detection and recognition for smart cars

Traffic sign detection and recognition system is becoming an essential component of smart cars. Speed-Limit Sign (SLS) is one of the most important traffic signs, since it is used to regulate the speed of vehicles in downtown and highways. The recognition of SLS by drivers is mandatory. In this paper, we investigate SLS detection and recognition system. We focus on North-American speed limit signs, including Canadian and U.S. signs. A modified version of Histogram of Oriented Gradients (HOG) is used to detect and recognize SLS through a set of two-level SVM-based classifiers. Moreover, we build our online database called North-American Speed Limit Signs (NASLS) which includes four SLS categories; white, yellow, black and orange signs. We show through an extensive set of experiments that our system achieves an accuracy of more than 94% of SLS recognition.

A Sequential Patterns Data Mining Approach Towards Vehicular Route Prediction in VANETs

Behavioral patterns prediction in the context of Vehicular Ad hoc Networks (VANETs) has been receiving increasing attention due to the enabling of on-demand, intelligent traffic analysis and real-time responses to traffic issues. One of these patterns, sequential patterns, is a type of behavioral pattern that describes the occurrence of events in a timely and ordered fashion. In the context of VANETs, these events are defined as an ordered list of road segments traversed by vehicles during their trips from a starting point to their final intended destination. In this paper, a new set of formal definitions depicting vehicular paths as sequential patterns is described. Also, five novel communication schemes have been designed and implemented under a simulated environment to collect vehicular paths; such schemes are classified under two categories: RSU (Road Side Unit)-based and Vehicle-based. After collection, extracted frequent paths are obtained through data mining, and the probability of these frequent paths is measured. In order to evaluate the effectiveness and efficiency of the proposed schemes, extensive experimental analysis has been realized.

Design of traffic sign detection, recognition, and transmission systems for smart vehicles

Traffic sign detection and recognition (TSDR) is an essential component of advanced driver assistance systems (ADAS). It is mainly designed to enhance driver safety through the fast acquisition and interpretation of traffic signs. However, such systems still suffer from the inability to accurately recognize signs. Moreover, the sharing of wirelessly recognized signs among vehicles is not yet supported by current systems. In some safety scenarios, vehicle-to-vehicle communication of traffic sign information is required. In this article, we first address challenges and undesirable factors facing TSDR systems. After that, we show how to design a TSDR system by addressing some useful techniques used in each stage of the system. For each stage, these techniques are regrouped into different categories. Then, for each category, a short description is given followed by some concluding remarks. Finally, the transmission of the recognized signs is briefly investigated.