Image Processing Algorithms for Driver Assistance using Wide Angle Cameras

Modern vehicles are deployed with a large number of sensors in order to provide a rich spectrum of driver assistance functionality. These systems enhance security and comfort of passengers and other traffic participants alike, but they also pave the road to fully autonomous traffic. In order to provide this functionality robustly and reliably, one currently makes use of numerous specialized sensors: laser, radar, ultrasound, and infrared sensors, as well as different kinds of video cameras. The diversity of sensors comes with high cost and enables complex assistance functions momentarily only for upper-class vehicles. The current research, thus, focuses on the development of better algorithms that make similar systems possible on inexpensive sensors. This thesis examines the aptitude of a new camera system, which has recently grown popular in vehicles of most of the large automobile manufacturers, for all major video-based driver assistance functionality. This so-called Topview system consists of four wide angle cameras with a view angle of up to 200 degrees, usually mounted at the front bumper, the two side mirrors and the trunk lid. By these means, one is able to provide a view surrounding the entire vehicle. However, the single camera images are distorted which substantiates the need for adapted image processing algorithms

Indian Traffic Signboard Recognition and Driver Alert System Using Machine Learning

Sign board recognition and driver alert system which has a number of important application areas that include advance driver assistance systems, road surveying and autonomous vehicles. This system uses image processing technique to isolate relevant data which is captured from the real time streaming video. The proposed method is broadly divided in five part data collection, data processing, data classification, training and testing. System uses variety of image processing techniques to enhance the image quality and to remove non-informational pixel, and detecting edges. Feature extracter are used to find the features of image. Machine learning algorithm Support Vector Machine(SVM) is used to classify the images based on their features. If features of sign that are captured from the video matches with the trained traffic signs then it will generate the voice signal to alert the driver. In India there are different traffic sign board and they are classified into three categories: Regulatory sign, Cautionary sign, informational sign. These Indian signs have four different shapes and eight different colors. The proposed system is trained for ten different types of sign . In each category more than a thousand sample images are used to train the network

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

Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness

In this modern era, land transports are increasing dramatically. Moreover, self-driven car or the Advanced Driving Assistance System (ADAS) is now the public demand. For these types of cars, road conditions detection is mandatory. On the other hand, compared to the number of vehicles, to increase the number of roads is not possible. Software is the only alternative solution. Road Conditions Detection system will help to solve the issues. For solving this problem, Image processing, and machine learning have been applied to develop a project namely, Detection of Road Conditions Using Image Processing and Machine Learning Techniques for Situation Awareness. Many issues could be considered for road conditions but the main focus will be on the detection of potholes, Maintenance sings and lane. Image processing and machine learning have been combined for our system for detecting in real-time. Machine learning has been applied to maintains signs detection. Image processing has been applied for detecting lanes and potholes. The detection system will provide a lane mark with colored lines, the pothole will be a marker with a red rectangular box and for a road Maintenance sign, the system will also provide information of aintenance sign as maintenance sing is detected. By observing all these scenarios, the driver will realize the road condition. On the other hand situation awareness is the ability to perceive information from it’s surrounding, takes decisions based on perceived information and it makes decision based on prediction

POL-LWIR Vehicle Detection: Convolutional Neural Networks Meet Polarised Infrared Sensors

For vehicle autonomy, driver assistance and situational awareness, it is necessary to operate at day and night, and in all weather conditions. In particular, long wave infrared (LWIR) sensors that receive predominantly emitted radiation have the capability to operate at night as well as during the day. In this work, we employ a polarised LWIR (POL-LWIR) camera to acquire data from a mobile vehicle, to compare and contrast four different convolutional neural network (CNN) configurations to detect other vehicles in video sequences. We evaluate two distinct and promising approaches, two-stage detection (Faster-RCNN) and one-stage detection (SSD), in four different configurations. We also employ two different image decompositions: the first based on the polarisation ellipse and the second on the Stokes parameters themselves. To evaluate our approach, the experimental trials were quantified by mean average precision (mAP) and processing time, showing a clear trade-off between the two factors. For example, the best mAP result of 80.94% was achieved using Faster-RCNN, but at a frame rate of 6.4 fps. In contrast, MobileNet SSD achieved only 64.51% mAP, but at 53.4 fps.Comment: Computer Vision and Pattern Recognition Workshop 201

IMACS:a framework for performance evaluation of image approximation in a closed-loop system

Image Processing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy and have long processing times. Image approximation has been proposed by recent works for an energy-efficient design of these applications. It also reduces the impact of long processing times. The challenge here is that the IP applications often work as a part of bigger closed-loop control systems, e.g. advanced driver assistance system (ADAS). The impact of image approximations that tolerate certain error on these image-based control (IBC) systems is very important. However, there is a lack of tool support to evaluate the performance of such closed-loop IBC systems when the IP is approximated. We propose a framework - for both software-in-the-loop (SiL) and hardware-in-the-loop (HiL) simulation - for performance evaluation of image approximation on a closed-loop automotive IBC system (IMACS). Both simulation setups model the 3D environment in 3ds Max, and simulate the system dynamics, camera position and environment in V-REP. Our SiL setup simulates the system software in C++ or Matlab. Here, V-REP runs as a server and the software as a client in synchronous mode. Our HiL simulation setup runs the system software in the NVIDIA Drive PX2 platform and communicates to V-REP using application programming interfaces (APIs) for synchronous execution. We show the effectiveness of our framework using a vision-based lateral control example

An evaluation framework for stereo-based driver assistance

This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km

Characterizing driving behavior using automatic visual analysis

In this work, we present the problem of rash driving detection algorithm using a single wide angle camera sensor, particularly useful in the Indian context. To our knowledge this rash driving problem has not been addressed using Image processing techniques (existing works use other sensors such as accelerometer). Car Image processing literature, though rich and mature, does not address the rash driving problem. In this work-in-progress paper, we present the need to address this problem, our approach and our future plans to build a rash driving detector.Comment: 4 pages,7 figures, IBM-ICARE201