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

Saber: window-based hybrid stream processing for heterogeneous architectures

Modern servers have become heterogeneous, often combining multicore CPUs with many-core GPGPUs. Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines. For an engine to exploit a heterogeneous architecture, it must execute streaming SQL queries with sufficient data-parallelism to fully utilise all available heterogeneous processors, and decide how to use each in the most effective way. It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. We describe SABER, a hybrid high-performance relational stream processing engine for CPUs and GPGPUs. SABER executes windowbased streaming SQL queries in a data-parallel fashion using all available CPU and GPGPU cores. Instead of statically assigning query operators to heterogeneous processors, SABER employs a new adaptive heterogeneous lookahead scheduling strategy, which increases the share of queries executing on the processor that yields the highest performance. To hide data movement costs, SABER pipelines the transfer of stream data between different memory types and the CPU/GPGPU. Our experimental comparison against state-ofthe-art engines shows that SABER increases processing throughput while maintaining low latency for a wide range of streaming SQL queries with small and large windows sizes

Multi-Sensor Data Fusion for Robust Environment Reconstruction in Autonomous Vehicle Applications

In autonomous vehicle systems, understanding the surrounding environment is mandatory for an intelligent vehicle to make every decision of movement on the road. Knowledge about the neighboring environment enables the vehicle to detect moving objects, especially irregular events such as jaywalking, sudden lane change of the vehicle etc. to avoid collision. This local situation awareness mostly depends on the advanced sensors (e.g. camera, LIDAR, RADAR) added to the vehicle. The main focus of this work is to formulate a problem of reconstructing the vehicle environment using point cloud data from the LIDAR and RGB color images from the camera. Based on a widely used point cloud registration tool such as iterated closest point (ICP), an expectation-maximization (EM)-ICP technique has been proposed to automatically mosaic multiple point cloud sets into a larger one. Motion trajectories of the moving objects are analyzed to address the issue of irregularity detection. Another contribution of this work is the utilization of fusion of color information (from RGB color images captured by the camera) with the three-dimensional point cloud data for better representation of the environment. For better understanding of the surrounding environment, histogram of oriented gradient (HOG) based techniques are exploited to detect pedestrians and vehicles.;Using both camera and LIDAR, an autonomous vehicle can gather information and reconstruct the map of the surrounding environment up to a certain distance. Capability of communicating and cooperating among vehicles can improve the automated driving decisions by providing extended and more precise view of the surroundings. In this work, a transmission power control algorithm is studied along with the adaptive content control algorithm to achieve a more accurate map of the vehicle environment. To exchange the local sensor data among the vehicles, an adaptive communication scheme is proposed that controls the lengths and the contents of the messages depending on the load of the communication channel. The exchange of this information can extend the tracking region of a vehicle beyond the area sensed by its own sensors. In this experiment, a combined effect of power control, and message length and content control algorithm is exploited to improve the map\u27s accuracy of the surroundings in a cooperative automated vehicle system

Improving Environment Detection by Behaviour Association for Context Adaptive Navigation

Navigation and positioning systems depend on both the operating environment and the behavior of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning, and the behavior can contribute additional information to the navigation solution. In order to operate across different contexts, a context‐adaptive navigation solution is required to detect the operating contexts and adopt different positioning techniques accordingly. This paper focuses on determining both environments and behaviors from smartphone sensors, serving for a context‐adaptive navigation system. Behavioral contexts cover both human activities and vehicle motions. The performance of behavior recognition in this paper is improved by feature selection and a connectivity‐dependent filter. Environmental contexts are detected from global navigation satellite system (GNSS) measurements. They are detected by using a probabilistic support vector machine, followed by a hidden Markov model for time‐domain filtering. The paper further investigates how behaviors can assist within the processes of environment detection. Finally, the proposed context‐determination algorithms are tested in a series of multicontext scenarios, showing that the proposed context association mechanism can effectively improve the accuracy of environment detection to more than 95% for pedestrian and more than 90% for vehicle

Data science applications to connected vehicles: Key barriers to overcome

The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor

Machine Learning based Vehicle Counting and Detection System

The study of how machines perceive instead of humans is known as vehicle detection or computer vision object identification. The primary purpose of a vehicle detection system is to identify one or multiple vehicles within the input images and live video feed. The dataset is used to train image processing algorithms for tasks like detection and tracking. To pinpoint the defects and strength of each image processing system, assessment criteria are used to develop, train, test, and compare them. To recognize, track, and count the vehicle in images and videos, the image processing algorithms such as CNN YOLOv3 and SVM are implemented. The main goal and intention of this work is to develop a system that can intelligently identify and track automobiles in still images and moving movies. The results demonstrated that CNN-based YOLOv3 does a  good job of detecting and tracking vehicles.   &nbsp

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Organic Computing in Off-highway Machines

Machine management systems in off-highway machines such as tractors or wheel loaders are designed for efficient operation and reduced fuel consumption in some predefined scenarios for which the machine has been developed. In this paper, we outline how concepts from Organic Computing may be used to realize a self-organizing, reliable, adaptive, and robust machine management system that is capable of adjusting to new situations. We propose an architecture for a machine management system based on the generic Observer/Controller architecture and focus on the structure of the Observer. Furthermore, we study the feasibility of working cycle detection by the Observer and analyze real and synthetic data from an off-highway machine. To extract features by which different working cycles of an off-highway machine can be distinguished, we employ principal component analysis