Inductive liquid level detection system Patent

Inductive liquid level detection syste

Visual feature tracking based on PHD filter for vehicle detection

Vehicle detection is one of the classical application among the Advance Driver Assistance Systems (ADAS). Applications like emergency braking or adaptive cruise control (ACC) require accurate and reliable vehicle detection. In latest years the improvements in vision detection have lead to the introduction of computer vision to detect vehicles by means of these more economical sensors, with high reliability. In the present paper, a novel algorithm for vehicle detection and tracking based on a probability hypothesis density (PHD) filter is presented. The first detection is based on a fast machine learning algorithm (Adaboost) and Haar-Like features. Later, the tracking is performed, by means features detected within the bounding box provided by the vehicle detection. The features, are tracked by a PHD filter. The results of the features being tracked are combined together in the last step, based on several different methods. Test provided show the performance of the PHD filter in public sequences using the different methods proposed.This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02)

Faster Pedestrian Recognition Using Deformable Part Models

Deformable part models achieve high precision in pedestrian recognition, but all publicly available implementations are too slow for real-time applications. We implemented a deformable part model algorithm fast enough for real-time use by exploiting information about the camera position and orientation. This implementation is both faster and more precise than alternative DPM implementations. These results are obtained by computing convolutions in the frequency domain and using lookup tables to speed up feature computation. This approach is almost an order of magnitude faster than the reference DPM implementation, with no loss in precision. Knowing the position of the camera with respect to horizon it is also possible prune many hypotheses based on their size and location. The range of acceptable sizes and positions is set by looking at the statistical distribution of bounding boxes in labelled images. With this approach it is not needed to compute the entire feature pyramid: for example higher resolution features are only needed near the horizon. This results in an increase in mean average precision of 5% and an increase in speed by a factor of two. Furthermore, to reduce misdetections involving small pedestrians near the horizon, input images are supersampled near the horizon. Supersampling the image at 1.5 times the original scale, results in an increase in precision of about 4%. The implementation was tested against the public KITTI dataset, obtaining an 8% improvement in mean average precision over the best performing DPM-based method. By allowing for a small loss in precision computational time can be easily brought down to our target of 100ms per image, reaching a solution that is faster and still more precise than all publicly available DPM implementations

PHD filter for vehicle tracking based on a monocular camera

Novel advance driver assistance systems, such as emergency braking and adaptive cruise control require the most reliable detection algorithms. Furthermore, in the recent years, the use of computer vision approaches in these type of applications is becoming more frequent. However, when dealing with these technologies, reliability is a very important factor that still requires improvement. On this paper, it is presented a tracking algorithm which aims in improving the accuracy of these applications, based on computer vision and modern Probability Hypothesis Density (PHD) Filter technique. The tracking is performed on the features detected within the bounding box provided by a computer video based vehicle detection algorithm. The features tracked are combined in a last stage, providing accurate monocular camera tracking. Test provided, allowed to identify the best method for feature combination. Furthermore, it was proved that under the proper visibility conditions, the PHD filter design is able to improve current methods such as Unscented Kalman Filter

360° Detection and tracking algorithm of both pedestrian and vehicle using fisheye images

All-around view is a mandatory element for autonomous vehicles. The European V-Charge project seeks to develop an autonomous vehicle using only low-cost sensors. This paper presents a detection and tracking algorithm that covers all the area around the vehicle using 4 fisheye cameras only. The algorithm is able to detect pedestrians and vehicles and track them, using cylindrical images. This paper presents the whole pipeline, from the image un-warping to the classification and the tracking algorithms, together with some results

Two-stage Part-Based Pedestrian Detection

Abstract — This paper introduces a part-based two-stage pedestrian detector. The system finds pedestrian candidates with an AdaBoost cascade on Haar-like features. It then verifies each candidate using a part-based HOG-SVM doing first a regression and then a classification based on the estimated function output from the regression. It uses the Histogram of Oriented Gradients (HOG) computed on both the full, upper and lower body of the candidates, and uses these in the final verification. The system has been trained and tested on the INRIA dataset and performs better than similar previous work, which uses full-body verification. I