IVC&ITS 2012 Abstracts
Full Papers
| Paper Nr: | 4 |
| Title: | A Warping Window Approach to Real-time Vision-based Pedestrian Detection in a Truck’s Blind Spot Zone |
| Authors: | Kristof Van Beeck, Toon Goedemé and Tinne Tuytelaars |
| Abstract: | We present a vision-based pedestrian tracking system targeting a very specific application: avoiding accidents in the blind spot zone of trucks. Existing blind spot safety systems do not offer a complete solution to this problem. Therefore we propose an active alarm system, which warns the truck driver if vulnerable road users occur in the blind spot zone. Our system is based solely on a vision sensor, and automatically detects vulnerable road users in the blind spot camera images. Due to the nature of this specific problem, this is a challenging task. Besides the demanding time constraint there is a need for a high accuracy, and we have to cope with the large distortion that a blind spot camera introduces. To achieve this we propose a warping window multi-pedestrian tracking algorithm. Our algorithm achieves real-time performance while maintaining high accuracy. To evaluate our algorithms we recorded several datasets with a real blind spot camera mounted on a real truck, consisting of realistic simulated dangerous blind spot situations. |
| Paper Nr: | 7 |
| Title: | Road Safety at Intersections Controlled by Traffic Lights - IVC and Risk Indexes |
| Authors: | Bruno Dalla Chiara, Francesco Paolo Deflorio and Serena Cuzzola |
| Abstract: | The paper reports the results of safety analyses conceived to assess the effects and benefits which might be generated by the forthcoming use of the infrastructure-to-vehicle (I2V) or vehicle-to-infrastructure (V2I) communication systems at road intersections regulated by traffic lights. Road crossings are often considered as critical areas for the occurrence of accidents, because they increase the likelihood of the event given the confluence of traffic streams from and to different directions. The analyses are aimed at calculating a real-time estimate of some risk indexes of accident, which might be provided on-board when approaching road intersection regulated by traffic lights. This information can then be used by an ADAS for traffic signal approaching. Two typologies of use of the information on the risk indexes can be identified: if data can be detected in real time, the driver could be informed on-board of a potentially hazardous situation using algorithms to predict the trend of the vehicle on the basis of the data detected from the monitoring; another use would be detecting – in case the vehicle were already within the dilemma zone – the lowest risk manoeuvre and sending a message on board to inform the driver. |
Posters
| Paper Nr: | 6 |
| Title: | Stereo Vision based On-road Vehicle Detection under Illumination Changing Conditions using Self Quotient Image |
| Authors: | Jonghwan Kim, Chung-Hee Lee and Young-Chul Lim |
| Abstract: | Today the many of automotive research groups study how to reduce vehicle accidents. For this reason, they have been developing the advanced driver assistance system (ADAS). In ADAS, the various sensors are used for recognizing the driving situations. For example, there are supersonic wave sensors and radar sensors and so on. In particular, in computer vision research groups, the vision sensors (ex. CCD, IR) are used for this. But it has some difficult problems because the vehicles are mainly driven in outdoors. The images captured by outdoors have various illumination conditions due to weather. It makes difficulty to detecting vehicles in images. In this paper, we introduce the vehicle detection method when the input images of system have illumination changes. We use the self quotient image (SQI) algorithm for illumination equalization. But SQI algorithm produces many false positive results. So we eliminate the false-positive results using stereo vision technique. In main section, we explain this method in detail. And we prove the proposed method has superior performance than existing systems using experiments. |