Computer Vision Based Traffic Monitoring and Analyzing From On-Road Videos

Traffic monitoring and traffic analysis is much needed to ensure a modern and convenient traffic system. However, it is a very challenging task as the traffic condition is dynamic which makes it quite impossible to maintain the traffic through traditional way. Designing a smart traffic system is also inevitable for the big and busy cities. In this paper, we propose a vision based traffic monitoring system that will help to maintain the traffic system smartly. We also generate an analysis of the traffic for a certain period, which will be helpful to design a smart and feasible traffic system for a busy city. In the proposed method, we use Haar feature based Adaboost classifier to detect vehicles from a video. We also count the number of vehicles appeared in the video utilizing two virtual detection lines (VDL). Detecting and counting vehicles by proposed method will provide an easy and cost effective solution for fruitful and operative traffic monitoring system along with information to design an efficient traffic model

Dataset Evaluation for Multi Vehicle Detection using Vision Based Techniques

Vehicle detection is one of the primal challenges of modern driver-assistance systems owing to the numerous factors, for instance, complicated surroundings, diverse types of vehicles with varied appearance and magnitude, low-resolution videos, fast-moving vehicles. It is utilized for multitudinous applications including traffic surveillance and collision prevention. This paper suggests a Vehicle Detection algorithm developed on Image Processing and Machine Learning. The presented algorithm is predicated on a Support Vector Machine(SVM) Classifier which employs feature vectors extracted via Histogram of Gradients(HOG) approach conducted on a semi-real time basis. A comparison study is presented stating the performance metrics of the algorithm on different datasets

Vehicle Type Recognition Combining Global and Local Features via Two-Stage Classification

This study proposes a new vehicle type recognition method that combines global and local features via a two-stage classification. To extract the continuous and complete global feature, an improved Canny edge detection algorithm with smooth filtering and non-maxima suppression abilities is proposed. To extract the local feature from four partitioned key patches, a set of Gabor wavelet kernels with five scales and eight orientations is introduced. Different from the single-stage classification, where all features are incorporated into one classifier simultaneously, the proposed two-stage classification strategy leverages two types of features and classifiers. In the first stage, the preliminary recognition of large vehicle or small vehicle is conducted based on the global feature via a k-nearest neighbor probability classifier. Based on the preliminary result, the specific recognition of bus, truck, van, or sedan is achieved based on the local feature via a discriminative sparse representation based classifier. We experiment with the proposed method on the public and established datasets involving various challenging cases, such as partial occlusion, poor illumination, and scale variation. Experimental results show that the proposed method outperforms existing state-of-the-art methods

Merge recommendations for driver assistance: A cross-modal, cost-sensitive approach

In this study, we present novel work focused on assisting the driver during merge maneuvers. We use an automotive testbed instrumented with sensors for monitoring critical regions in the vehicle's surround. Fusing information from multiple sensor modalities, we integrate measurements into a contextually relevant, intuitive, general representation, which we term the Dynamic Probabilistic Drivability Map [DPDM]. We formulate the DPDM for driver assistance as a compact representation of the surround environment, integrating vehicle tracking information, lane information, road geometry, obstacle detection, and ego-vehicle dynamics. Given a robust understanding of the ego-vehicle's dynamics, other vehicles, and the on-road environment, our system recommends merge maneuvers to the driver, formulating the maneuver as a dynamic programming problem over the DPDM, searching for the minimum cost solution for merging. Based on the configuration of the road, lanes, and other vehicles on the road, the system recommends the appropriate acceleration or deceleration for merging into the adjacent lane, specifying when and how to merge

An Image Processing Algorithm for Vehicle Detection and Tracking

Object detection ,tracking and its velocity estimation play important roles in trac surveillance system.It's an arising and On-Demand technique in the eld of image processing . Real time object track is vital job in many computer vision practical application.Two major components can be distinguished in a typical visual tracker.The picture determination of the feature accessible from most movement camera framework is low. As a rule for following multi item, recognizing them from another isn't simple due to their comparability. In this paper we depict a system, for tracking various items, where the articles are vehicles. The quantity of vehicles is obscure and diers from frame to frame. We identify all moving objects, and for tracking of vehicle we utilize the kalman lter and shading highlight and separation of it from one casing to the following. So the system can recognize and follow all vehicles exclusively. The proposed method can be utilized in numerous moving items. The proposed algorithm uses kalman lter to estimate objects' location,shape and size in a small range and to gain trajectory a of the object. To make visible moving object prominently it is required to remove the noise in the image which is done by morphological operations.Objects are tracked by feature matching in image sequence.Matched objects are numbered same .Then number of objects crossing through a particular line in the image are counted. Finally to gure out rush in trac area velocity is estimated by averaging the speed of vehicles present in a frame. If average velocity accedes a threshold value,that means heavy trac is there. Through out the process CCTV camera is considered stationary

Compute-proximal Energy Harvesting for Mobile Environments: Fundamentals, Applications, and Tools

Over the past two decades, we have witnessed remarkable achievements in computing, sensing, actuating, and communications capabilities of ubiquitous computing applications. However, due to the limitations in stable energy supply, it is difficult to make the applications ubiquitous. Batteries have been considered a promising technology for this problem, but their low energy density and sluggish innovation have constrained the utility and expansion of ubiquitous computing. Two key techniques—energy harvesting and power management—have been studied as alternatives to overcome the battery limitations. Compared to static environments such as homes or buildings, there are more energy harvesting opportunities in mobile environments since ubiquitous systems can generate various forms of energy as they move. Most of the previous studies in this regard have been focused on human movements for wearable computing, while other mobile environments (e.g., cars, motorcycles, and bikes) have received limited attention. In this thesis, I present a class of energy harvesting approaches called compute-proximal energy harvesting, which allows us to develop energy harvesting technology where computing, sensing, and actuating are needed in vehicles. Computing includes sensing phenomena, executing instructions, actuating components, storing information, and communication. Proximal considers the harvesting of energy available around the specific location where computation is needed, reducing the need for excessive wiring. A primary goal of this new approach is to mitigate the effort associated with the installation and field deployment of self-sustained computing and lower the entry barriers to developing self-sustainable systems for vehicles. In this thesis, I first select an automobile as a promising case study and discuss the opportunities, challenges, and design guidelines of compute-proximal energy harvesting with practical yet advanced examples in the automotive domain. Second, I present research in the design of small-scale wind energy harvesters and the implementation and evaluation of two advanced safety sensing systems—a blind spot monitoring system and a lane detection system—with the harvested power from wind. Finally, I conduct a study to democratize the lessons learned from the automotive case studies for makers and people with no prior experience in energy harvesting technology. In this study, I seek to understand what problems they have encountered and what possible solutions they have considered while dealing with energy harvesting technology. Based on the findings, I develop a comprehensive energy harvesting toolkit and examine its utility, usability, and creativity through a series of workshops.Ph.D

An intra-vehicular wireless multimedia sensor network for smartphone-based low-cost advanced driver-assistance systems

Advanced driver-assistance systems (ADAS) are more prevalent in high-end vehicles than in low-end vehicles. The research proposes an alternative for drivers without having to wait years to gain access to the safety ADAS offers. Wireless Multimedia Sensor Networks (WMSN) for ADAS applications in collaboration with smartphones is non-existent. Intra-vehicle environments cause difficulties in data transfer for wireless networks where performance of such networks in an intra-vehicle network is investigated. A low-cost alternative was proposed that extends a smartphone’s sensor perception, using a camera- based wireless sensor network. This dissertation presents the design of a low-cost ADAS alternative that uses an intra-vehicle wireless sensor network structured by a Wi-Fi Direct topology, using a smartphone as the processing platform. In addition, to expand on the smartphone’s other commonly available wireless protocols, the Bluetooth protocol was used to collect blind spot sensory data, being processed by the smartphone. Both protocols form part of the Intra-Vehicular Wireless Sensor Network (IVWSN). Essential ADAS features developed on the smartphone ADAS application carried out both lane detection and collision detection on a vehicle. A smartphone’s processing power was harnessed and used as a generic object detector through a convolution neural network, using the sensory network’s video streams. Blind spot sensors on the lateral sides of the vehicle provided sensory data transmitted to the smartphone through Bluetooth. IVWSNs are complex environments with many reflective materials that may impede communication. The network in a vehicle environment should be reliable. The network’s performance was analysed to ensure that the network could carry out detection in real-time, which would be essential for the driver’s safety. General ADAS systems use wired harnessing for communication and, therefore, the practicality of a novel wireless ADAS solution was tested. It was found that a low-cost advanced driver-assistance system alternative can be conceptualised by using object detection techniques being processed on a smartphone from multiple streams, sourced from an IVWSN, composed of camera sensors. A low-cost CMOS camera sensors network with a smartphone found an application, using Wi-Fi Direct to create an intra-vehicle wireless network as a low-cost advanced driver-assistance system.Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021.Electrical, Electronic and Computer EngineeringMEng (Computer Engineering)Unrestricte

DEVELOPMENT OF A NOVEL VEHICLE GUIDANCE SYSTEM: VEHICLE RISK MITIGATION AND CONTROL

Over a half of fatal vehicular crashes occur due to vehicles leaving their designated travel lane and entering other lanes or leaving the roadway. Lane departure accidents also result in billions of dollars in cost to society. Recent vehicle technology research into driver assistance and vehicle autonomy has developed to assume various driving tasks. However, these systems are do not work for all roads and travel conditions. The purpose of this research study was to begin the development a novel vehicle guidance approach, specifically studying how the vehicle interacts with the system to detect departures and control the vehicle A literature review was conducted, covering topics such as vehicle sensors, control methods, environment recognition, driver assistance methods, vehicle autonomy methods, communication, positioning, and regulations. Researchers identified environment independence, recognition accuracy, computational load, and industry collaboration as areas of need in intelligent transportation. A novel method of vehicle guidance was conceptualized known as the MwRSF Smart Barrier. The vision of this method is to send verified road path data, based AASHTO design and vehicle dynamic aspects, to guide the vehicle. To further development research was done to determine various aspects of vehicle dynamics and trajectory trends can be used to predict departures and control the vehicle. Tire-to-road friction capacity and roll stability were identified as traits that can be prevented with future road path knowledge. Road departure characteristics were mathematically developed. It was shown that lateral departure, orientation error, and curvature error are parametrically linked, and discussion was given for these metrics as the basis for of departure prediction. A three parallel PID controller for modulating vehicle steering inputs to a virtual vehicle to remain on the path was developed. The controller was informed by a matrix of XY road coordinates, road curvature and future road curvature and was able to keep the simulated vehicle to within 1 in of the centerline target path. Recommendations were made for the creation of warning modules, threshold levels, improvements to be applied to vehicle controller, and ultimately full-scale testing. Advisor: Cody S. Stoll