Vision-Based Blind Spot Detection Using Optical Flow

Lecture Notes in Computer Science, 2011

The goal of this work is to propose a solution to improve a driver's safety while changing lanes on the highway. In fact, if the driver is not aware of the presence of a vehicle in his blindspot a crash can occur. In this article we propose a method to monitor the blindspot zone using video feeds and warn the driver of any dangerous situation. In order to fit in a real time embedded car safety system, we avoid using any complex techniques such as classification and learning. The blindspot monitoring algorithm we expose here is based on a features tracking approach by optical flow calculation. The features to track are chosen essentially given their motion patterns that must match those of a moving vehicle and are filtered in order to overcome the presence of noise. We can then take a decision on a car presence in the blindspot given the tracked features density. To illustrate our approach we present some results using video feeds captured on the highway.

Computer Standards & Interfaces, 1999

We describe an optical flow based obstacle detection system for use in detecting vehicles approaching the blind spot of a car on highways and city streets. The system runs at near frame rate (8-15 frames/second) on PC hardware. We will discuss the prediction of a camera image given an implicit optical flow field and comparison with the actual camera image. The advantage to this approach is that we never explicitly calculate optical flow. We will also present results on digitized highway images, and video taken from Navlab 5 while driving on a Pittsburgh highway.

2005

One of the Advanced Driver Assistance Systems for Intelligent Vehicles havs to deal with the detection and tracking of other vehicles. It will have many applications: Platooning, Stop&go, Blind angle perception, Manoeuvres supervisor. In this paper a system based on computer vision is presented. A geometric model of the vehicle is defined where its energy function includes information of the shape and symmetry of the vehicle and the shadow it produces. A genetic algorithm finds the optimum parameter values. Examples of real images are shown to validate the algorithm.

2007

In this paper it is described a solution to detect wrong way drivers on highways. The proposed solution is based on three main stages: Learning, Detection and Validation. In the first stage, the orientation pattern of vehicles motion flow is learned and modelled by a mixture of gaussians. The second stage (Detection and Temporal Validation) makes use of the learned orientation model to detect objects moving on lane's opposite direction. The third and final stage uses an Appearance based approach to ensure the detection of a vehicle before triggering an alarm. This methodology has proven to be quite robust in terms of different weather conditions, illumination and image quality. Some experiments carried out with several movies from traffic surveillance cameras on highways show the robustness of the proposed solution.

IEEE Transactions on Robotics and Automation, 1998

This paper describes procedures for obtaining a reliable and dense optical flow from image sequences taken by a television (TV) camera mounted on a car moving in usual outdoor scenarios. The optical flow can be computed from these image sequences by using several techniques. Differential techniques to compute the optical flow do not provide adequate results, because of a poor texture in images and the presence of shocks and vibrations experienced by the TV camera during image acquisition. By using correlation based techniques and by correcting the optical flows for shocks and vibrations, useful sequences of optical flows can be obtained. When the car is moving along a flat road and the optical axis of the TV camera is parallel to the ground, the motion field is expected to be almost quadratic and have a specific structure. As a consequence the egomotion can be estimated from this optical flow and information on the speed and the angular velocity of the moving vehicle are obtained. By analyzing the optical flow it is possible to recover also a coarse segmentation of the flow, in which objects moving with a different speed are identified. By combining information from intensity edges a better localization of motion boundaries are obtained. These results suggest that the optical flow can be successfully used by a vision system for assisting a driver in a vehicle moving in usual streets and motorways.

2008

This report provides a brief and informal introduction into stereo and motion analysis for driver assistance. Stereo and motion analysis play a central role in computer vision . Many algorithms in this field have been proposed and carefully studied; see, for example, and the website vision.middlebury.edu for stereo and optic flow algorithms.

Journal of Informatics Electrical and Electronics Engineering (JIEEE), 2021

This paper contains the details of different object detection (OD) techniques, object identification's relationship with video investigation, and picture understanding, it has pulled in much exploration consideration as of late. Customary item identification strategies are based on high-quality highlights and shallow teachable models. This survey paper presents one such strategy which is named as Optical Flow method (OFM). This strategy is discovered to be stronger and more effective for moving item recognition and the equivalent has appeared by an investigation in this review paper. Applying optical stream to a picture gives stream vectors of the focuses compared to the moving items. Next piece of denoting the necessary moving object of interest checks to the post-preparing. Post handling is the real commitment of the review paper for moving item identification issues. Their presentation effectively deteriorates by developing complex troupes which join numerous low-level pictur...

IEEE Transactions on Intelligent Transportation Systems, 2016

This paper proposes a novel approach for detecting and tracking vehicles to the rear and in the blind zone of a vehicle, using a single rear-mounted fisheye camera and multiple detection algorithms. A maneuver that is a significant cause of accidents involves a target vehicle approaching the host vehicle from the rear and overtaking into the adjacent lane. As the overtaking vehicle moves toward the edge of the image and into the blind zone, the view of the vehicle gradually changes from a front view to a side view. Furthermore, the effects of fisheye distortion are at their most pronounced toward the extremities of the image, rendering detection of a target vehicle entering the blind zone even more difficult. The proposed system employs an AdaBoost classifier at distances of 10-40 m between the host and target vehicles. For detection at short distances where the view of a target vehicle has changed to a side view and the AdaBoost classifier is less effective, identification of vehicle wheels is proposed. Two methods of wheel detection are employed: at distances between 5 and 15 m, a novel algorithm entitled wheel arch contour detection (WACD) is presented, and for distances less than 5 m, Hough circle detection provides reliable wheel detection. A testing framework is also presented, which categorizes detection performance as a function of distance between host and target vehicles. Experimental results indicate that the proposed method results in a detection rate of greater than 93% in the critical range (blind zone) of the host.

Communications on Applied Electronics, 2015

In recent years, automotive manufacturers have equipped their vehicles with innovative Advanced Driver Assistance Systems (ADAS) to ease driving and avoid dangerous situations, such as unintended lane departures or collisions with other road users, like vehicles and pedestrians. To this end, ADAS at the cutting edge are equipped with cameras to sense the vehicle surrounding. This research work investigates the techniques for monocular vision based vehicle detection. A system that can robustly detect and track vehicles in images. The system consists of three major modules: shape analysis based on Histogram of oriented gradient (HOG) is used as the main feature descriptor, a machine learning part based on support vector machine (SVM) for vehicle verification, lastly a technique is applied for texture analysis by applying the concept of gray level co-occurrence matrix (GLCM). More specifically, we are interested in detection of cars from different camera viewpoints, diverse lightning conditions majorly images in sunlight, night, rain, normal day light, low light and further handling the occlusion. The images has been pre-processed at the first step to get the optimum results in all the conditions. Experiments have been conducted on large numbers of car images with different angles. For car images the classifier contains 4 classes of images with the combination of positive and negative images, the test and train segments. Due to length of long feature vector we have deduced it using different cell sizes for more accuracy and efficiency. Results will be presented and future work will be discussed.

In this paper, an enhanced optical flow analysis based moving vehicle detection and tracking system has been developed. A novel multidirectional brightness-intensity constraints (MBIGC) estimation and fusion based optical flow analysis (MDFOA) technique has been proposed that performs simultaneous pixel's intensity and velocity estimation in a moving frame for detecting and tracking the moving vehicle. The conventional Lucas Kanade and Horn Schunck optical flow analysis algorithms have been enhanced by incorporating a multidirectional BIGC estimation, which has been further enriched with a non-linear adaptive median filter based denoising. Such novelties have significantly enhanced the video segmentation and detection. A vector magnitude threshold based MDOFA algorithm has been developed for motion vector retrieval that eventually enables swift and precise moving vehicle segmentation from the background frame. A heuristic filtering based blog analysis has been applied for vehicle tracking. The MATLAB based simulation reveals that MDFOA-HS outperforms LK in terms of execution time and detection accuracy. In addition, the accurate traffic density estimation affirms robustness of the proposed system to be used in intelligent transport system.