Figure 3 Grouping of close clusters in a second clustering stage
Related Figures (4)
Fig. 1. Flow Diagram of the Blind-spot detection algorithm puting at image level, pixel-wise clustering, analysis of clusters and final vehicle detection. As previously stated, the system relies on the computation of optical flow using vision as main sensor providing information about the road scene. In order to reduce computational time, optical flow is computed only on relevant points in the image. These points are characterized for exhibiting certain features that permit to discriminate them from the rest of point in their environment. Normally, these salient features have prominent values of energy, entropy, or similar statistics. In this work, a salient feature point has been considered as that exhibiting a relevant differential value. Accordingly, a Canny edge extractor is applied to the original incoming image. Pixels providing a positive value after the Canny filter are considered for calculation of optical flow. The reason for this relies on the fact that relevant points are needed for optical flow computation since matching of the points have to be done between two consecutive frames. Fig. 2. Clustering of pixels providing relevant optical flow Fig. 4. Pre-detection Flow Diagram Fig. 5. Example of blind spot detection in a sequence of images. The indicator in the upper- right part of the figure toggles from green to blue when a car is detected in the blind spot. A digital camera was mounted in the lateral mirror of a real car equipped with a Pen- tium IV 2.8 GHz PC running Linux Knoppix 3.7 and OpenCV libraries 0.9.6. The car
