Gerhard Gritsch

Embedded vehicle speed estimation system using an asynchronous temporal contrast vision sensor

This article presents an embedded multilane traffic data acquisition system based on an asynchronous temporal contrast vision sensor, and algorithms for vehicle speed estimation developed to make efficient use of the asynchronous high-precision timing information delivered by this sensor. The vision sensor features high temporal resolution with a latency of less than 100 μ s, wide dynamic range of 120 dB of illumination, and zero-redundancy, asynchronous data output. For data collection, processing and interfacing, a low-cost digital signal processor is used. The speed of the detected vehicles is calculated from the vision sensors asynchronous temporal contrast event data. We present three different algorithms for velocity estimation and evaluate their accuracy by means of calibrated reference measurements. The error of the speed estimation of all algorithms is near zero mean and has a standard deviation better than 3% for both traffic flow directions. The results and the accuracy limitations as well as the combined use of the algorithms in the system are discussed.

Vehicle Counting with an Embedded Traffic Data System using an Optical Transient Sensor

In this paper a sensor system for traffic data acquisition is presented. The embedded system, comprising a motion-sensitive optical sensor and a low-cost, low-power DSP, is capable of detecting, counting and measuring the velocity of passing vehicles. The detection is based on monitoring of the optical sensor output within configurable regions of interest in the sensors field-of-view. In particular in this work we focus on the evaluation of the applied vehicle counting algorithm. The verification of the acquired data is based on manually annotated traffic data of 360 minutes length, containing a total of about 7000 vehicles. The counting error is determined for short (3 minutes) and long (60 minutes) time intervals. The calculated error of 99,2% of the short time intervals and 100% of the long time intervals analyzed, remain within commonly recognized margins of 10% and 3% of detection error respectively.

Night-time vehicle classification with an embedded, vision system

The paper presents night-time vehicle classification using an embedded vision system based on an optical transient sensor. This neuromorphic sensor features an array of 128×128 pixels that respond to relative light intensity changes with low latency and high dynamic range. The proposed algorithm exploits the temporal resolution and sparse representation of the data, delivered by the sensor in the data-driven Address-Event Representation (AER) format, to efficiently implement a robust classification of vehicles into two classes, car-like and truck-like, during night-time operation. The classification is based on the extraction of the positions and distances of the vehicles head lights to estimate vehicle width. We present the algorithm, test data and an evaluation of the classification accuracy by comparison of the test data with ground truth from video annotation and reference results from a state-of-the-art ultrasonic/radar-combination reference detector. The results show that the difference in total truck counts with respect to a reference detector and to manually annotated video during nighttime operation under dry and wet road conditions is typically below 6%.

Combining time series and frequency domain analysis for a automatic seizure detection

The detection of epileptic seizures in long-term electroencephalographic (EEG) recordings is a time-consuming and tedious task requiring specially trained medical experts. The EpiScan [1-4] seizure detection algorithm developed by the Austrian Institute of Technology (AIT) has proven to achieve high detection performance with a robust false alarm rate in the clinical setting. This paper introduces a novel time domain method for detection of epileptic seizure patterns with focus on irregular and distorted rhythmic activity. The method scans the EEG for sequences of similar epileptiform discharges and uses a combination of duration and similarity measure to decide for a seizure. The resulting method was tested on an EEG database with 275 patients including over 22000h of unselected and uncut EEG recording and 623 seizures. Used in combination with the EpiScan algorithm we increased the overall sensitivity from 70% to 73% while reducing the false alarm rate from 0.33 to 0.30 alarms per hour.

Automatic detection of the seizure onset zone based on ictal EEG

In this paper we show a proof of concept for novel automatic seizure onset zone detector. The proposed approach utilizes the Austrian Institute of Technology (AIT) seizure detection system EpiScan extended by a frequency domain source localization module. EpiScan was proven to detect rhythmic epileptoform seizure activity often seen during the early phase of epileptic seizures with reasonable high sensitivity and specificity. Additionally, the core module of EpiScan provides complex coefficients and fundamental frequencies representing the rhythmic activity of the ictal EEG signal. These parameters serve as input to a frequency domain version of the Minimum Variance Beamformer to estimate the most dominant source. The position of this source is the detected seizure onset zone. The results are compared to a state of the art wavelet transformation approach based on a manually chosen frequency band. Our first results are encouraging since they coincide with those obtained with the wavelet approach and furthermore show excellent accordance with the medical report for the majority of analyzed seizures. In contrast to the wavelet approach our method has the advantage that it does not rely on a manual selection of the frequency band.

Spatial correlation based artifact detection for automatic seizure detection in EEG

Automatic EEG-processing systems such as seizure detection systems are more and more in use to cope with the large amount of data that arises from long-term EEG-monitorings. Since artifacts occur very often during the recordings and disturb the EEG-processing, it is crucial for these systems to have a good automatic artifact detection. We present a novel, computationally inexpensive automatic artifact detection system that uses the spatial distribution of the EEG-signal and the location of the electrodes to detect artifacts on electrodes. The algorithm was evaluated by including it into the automatic seizure detection system EpiScan and applying it to a very large amount of data including a large variety of EEGs and artifacts.

High density wireless EEG prototype: Design and evaluation against reference equipment.

A high density wireless electroencephalographic (EEG) platform has been designed. It is able to record up to 64 EEG channels with electrode to tissue impedance (ETI) monitoring. The analog front-end is based on two kinds of low power ASICs implementing the active electrodes and the amplifier. A power efficient compression algorithm enables the use of continuous wireless transmission of data through Bluetooth for real-time monitoring with an overall power consumption of about 350 mW. EEG acquisitions on five subjects (one healthy subject and four patients suffering from epilepsy) have been recorded in parallel with a reference system commonly used in clinical practice and data of the wireless prototype and reference system have been processed with an automatic tool for seizure detection and localization. The false alarm rates (0.1-0.5 events per hour) are comparable between the two system and wireless prototype also detected the seizure correctly and allowed its localization.

Automated Vehicle Velocity Estimation Using a Dual-Line Asynchronous Sensor

This paper proposes a vision system for vehicle velocity estimation based on asynchronous output data. For real-time velocity estimation, a processing method has been developed and implemented on a digital signal processor. It exploits the sparse asynchronous data representation from the dual-line sensor with high temporal resolution (better than 100 mus) and low latency. The processing concept includes vehicle contours extraction, velocity estimation and scaling. The estimation approach uses the vehicles leading edge on both lines to extract the detection time difference, which is used for the vehicle velocity estimation. The dual-line sensor system including this processing concept has been evaluated on several vehicles for velocities ranging from 10 to 120 km/h. The experiments have been performed at a test site with a two-lane road and the results obtained show an absolute velocity estimation error < 4 km/h.

Ein innovatives, optisches Sensorsystem für die Verkehrsdatenerfassung.

ZusammenfassungDer Artikel beschreibt ein eingebettetes, optisches Sensorsystem, das auf einem speziellen optischen CMOS-Sensor mit analoger Signalvorverarbeitung basiert. Die Daten werden asynchron mit dem so genannten Address-event-Protokoll vom Sensor zur nachgeschalteten digitalen Signalverarbeitung übertragen. Dank der effizienten Datenkodierung und Signalvorverarbeitung, die bewegte Objekte aus der Szene extrahiert, erreicht die Zeitauflösung der digitalen Datenverarbeitung auf einem kostengünstigen digitalen Signalprozessor eine Millisekunde. Die Algorithmen zur Verkehrsdatenerfassung berechnen in Echtzeit die Verkehrsstärke, Fahrgeschwindigkeit, Nettozeitlücke und Belegung auf bis zu vier Fahrstreifen simultan. Der Artikel präsentiert Verkehrsdaten, die an einer vierspurigen Autobahn über vier Tage aufgenommen wurden. Die Zeitverläufe der Verkehrsstärke, der mittleren Fahrgeschwindigkeit sowie der mittleren Nettozeitlücke und der mittleren Belegung wurden in Fünfminutenintervallen ausgewertet. Der Fehler in der Geschwindigkeitsschätzung liegt unter 3 %, die Genauigkeit der Fahrzeugzählung liegt, bei Tageslicht und Normalbedingungen, über 97 %.SummaryAn embedded vision system based on a specialized CMOS optical sensor with on-chip analogue signal pre-processing is described. The sensor signal is transmitted to digital processing units via asynchronous address-event protocol. Using the efficient data coding and signal pre-processing concept, moving objects can be extracted from the visual scene with a temporal resolution of 1 millisecond. The traffic data acquisition algorithms compute traffic flow, vehicle velocity, separation and lane occupancy in real-time on up to four lanes simultaneously. We present traffic data that has been acquired continuously over four days at a test site at a four lane highway. The error of the velocity estimation is below 3 %, the precision of the vehicle flow measurement is better than 97 % under normal daylight conditions.

Automatic ictal onset source localization in presurgical epilepsy evaluation

OBJECTIVE To test the diagnostic accuracy of a new automatic algorithm for ictal onset source localization (IOSL) during routine presurgical epilepsy evaluation following STARD (Standards for Reporting of Diagnostic Accuracy) criteria. METHODS We included 28 consecutive patients with refractory focal epilepsy (25 patients with temporal lobe epilepsy (TLE) and 3 with extratemporal epilepsy) who underwent resective epilepsy surgery. Ictal EEG patterns were analyzed with a novel automatic IOSL algorithm. IOSL source localizations on a sublobar level were validated by comparison with actual resection sites and seizure free outcome 2 years after surgery. RESULTS Sensitivity of IOSL was 92.3% (TLE: 92.3%); specificity 60% (TLE: 50%); positive predictive value 66.7% (TLE: 66.7%); and negative predictive value 90% (TLE: 85.7%). The likelihood ratio was more than ten times higher for concordant IOSL results as compared to discordant results (p = 0.013). CONCLUSIONS We demonstrated the clinical feasibility of our IOSL approach yielding reasonable high performance measures on a sublobar level. SIGNIFICANCE Our IOSL method may contribute to a correct localization of the seizure onset zone in temporal lobe epilepsy and can readily be used in standard epilepsy monitoring settings. Further studies are needed for validation in extratemporal epilepsy.