The efficient monitoring of traffic flow as well as related surveillance and detection applications demand an increasingly robust recognition of vehicles in image and video data. This paper describes two different methods for vehicle detection in real world situations: Principal Component Analysis and the Histogram of Gradients principle. Both methods are described and their detection capabilities as well as advantages and disadvantages are compared. A large sample dataset which contains images of cars from the backside and frontside in day and night conditions is the basis for creating and optimizing both variants of the proposed algorithms. The resulting two detectors allow recognition of vehicles in frontal view +- 30 deg and views from behind +- 30 deg. The paper demonstrates that both detection methods can operate effectively even under difficult lighting situations with high detection rates and a low number of false positives.
State of the art algorithms for people or vehicle detection should not only be accurate in terms of detection performance and low false alarm rate, but also fast enough for real time applications. Accurate algorithms are usually very complex and tend to have a lot of calculated features to be used or parameters available for adjustments. So one big goal is to decrease the amount of necessary features used for object detection while increasing the speed of the algorithm and overall performance by finding an optimum set of classifier variables. In this paper we describe algorithms for feature selection, parameter optimisation and pattern matching especially for the task of pedestrian detection based on Histograms of Oriented Gradients and Support Vector Machine classifiers. Shape features were derived with the Histogram of Oriented Gradients algorithm which resulted in a feature vector of 6318 elements. To decrease computation time to an acceptable limit for real-time detection we reduced the full feature vector to sizes of 1000, 500, 300, 200, and 160 elements with a genetic feature selection method. With the remaining features a Support Vector Machine classifier was build and its classification parameters further optimized to result in less support vectors for further improvements in processing speed. This paper compares the classification performance, of the different SVM's on real videos (some sample images), visualizes the chosen features (which histogram bins on which location in the image search feature) and analyses the performance of the final system with respect to execution time and frame rate.
The availability of reliable ultrafast laser systems and their unique properties for material processing are the basis for new lithographic methods in the sector of micro- and nanofabrication processes such as two-photon 3D-lithography. Beside its flexibility, one of the most powerful features of this technology is the true 3D structuring capability, which allows fabrication with higher efficiency and with higher resolution compared to a sequential layer-by-layer structuring and build-up technique. Up to now, the two-photon method was mainly used for writing 3D structures quasi anywhere inside a bulk volume. In combination with a sophisticated and versatile machine vision support, the two-photon 3D-lithography is now targeting for micro- and nano-optical applications and the integration of optical and photonic components into optical microsystems.
We report on a disruptive improvement of this lithographic method by means of an optical detection system for optical components (e.g. laser diode chips / LEDs and photo diodes) that are already assembled on an optical micropackage. The detection system determines the position coordinates of features of the optical microsystem in all three dimensions with micrometer resolution, combining digital image processing and evaluation of back reflected laser light from the surface of the system. This information is subsequently processed for controlling the fabrication of directly laser written optical and photonic structures inside and around such an optical microsystem. The strong advantage of this approach lies in its adaptation of laser written structures to existing features and structures, which also permits to compensate for misalignments and imperfections of preconfigured packages.